Are you tired of playing keyword whack-a-mole? Does it feel like you’re stuck in a semantic maze when trying to optimize pages for search? Perhaps it’s time to go beyond keywords and tap into the true potential of language modeling for SEO.
This extensive guide will explain how modern linguistic analysis techniques used by sophisticated AI systems can take search engine comprehension to the next level. We’ll cover 63 different methods spanning semantics, syntax, sentiment, entities, knowledge graphs, and much more. We don’t know if Google, Bing, and other search engines use all the large language models (LLM) covered here,but as LLM takes on a greater role in the SERPs, understanding LLM becomes more important for every SEO. This is a primer designed for you to expand upon, inspire ideation, and hopefully report back what you discovered if you implemented an action based on a particular model.
Fair warning: This is not a quick tip listicle. The full article spans over 13,000 words and multiple sections. We’ll be digging deep into advanced NLP capable of decoding meanings from text through machine learning.
But the investment of your time and brainpower will pay off with unique insights you can apply to enhance on- and off-page optimization. You’ll learn how search engines may leverage these techniques under the hood today and where future relevance ranking might be headed. We even included some practical things to try, but we fully expect you to extrapolate and try your own optimizations based on the given model.
Arm yourself with linguistics fundamentals to create content ready for the next generation of semantic search. Build pages that don’t just match keywords but actually communicate knowledge. Move from targeting only search bots to delivering value for humans.
The details matter when it comes to comprehension. And details are what this guide delivers. So grab a nice beverage, get comfortable, and level up your optimization strategy with the techniques savvy AI engineers use to extract meaning from language. The effort will give you a valuable competitive edge.
The 63 Language Models With Examples and Actionables for SEOs.
We are presenting these models in somewhat of a prioritized manner rather than alphabetically. Note the word, “somewhat,” as endless arguments between SEOs might emerge between the actual order of this list. This is simply a semi-prioritized list based on how we are seeing things currently.
Each model follows the following pattern:
- Name of the Model.
- Super short definition and example of the model at work
- A longer, more technical description of the model.
- Examples of how a search engine might use this model to help determine ranking and query matching.
- An idea kickstarter. These are NOT concrete do these items. These are hypothetical: IF this model were ever to be used, then here are some ideas of practical steps you could take to improve your page’s/site’s presentation for that particular model to reward your site in ranking. They should spawn you onward in your own thinking.
Conceptual Semantic Lexical Relations
This looks at how concepts, word meanings, and vocabulary connect. For example, it relates the concept of “transportation” to words like “car” and “train”.
Search engines rely heavily on understanding the conceptual, semantic, and lexical relationships within web pages to properly interpret their content. Analyzing these different dimensions of meaning can reveal the key topics, entities, and themes that characterize a page. For example, mapping out lexical relations between words can uncover synonyms and related terms that indicate core concepts. Identifying semantic relations between entities mentioned on a page provides insight into what real-world objects and events the content describes. Modeling conceptual relations gives a broader view of the main ideas and abstractions that underly the text.
Practical examples:
- Recognizing synonyms and hypernyms during lexical analysis could help aggregate evidence for pages about a topic, even if they don’t share exact keyword matches.
- Identifying meronym semantic relationships could boost pages that comprehensively cover parts of a larger concept.
- Modeling conceptual relations may reveal pages with more abstract, high-level relevance to a topic instead of just literal word matches.
Three Potential Actionable SEO Ideas
- Expand page wording with synonyms and hypernyms of key concepts to improve topical keyword density.
- Structure content using lexical chaining techniques to tie together semantically related terms.
- Optimize site architecture so pages focused on narrower concepts link into hubs about broader topics.
Cross Pos Relations
This analyzes how different parts of speech, like nouns and verbs, link together in sentences. For example, it looks at how nouns connect to verbs.
Analyzing the relationships between parts of speech used on a webpage can reveal important clues about its information content. Research shows distinctive patterns of nouns, verbs, adjectives and other grammatical classes within text of differing subject matter, style and sentiment. Leveraging these cross-part-of-speech relationships allows search engines to categorize and compare pages based on their structural composition. For instance, some topical areas feature heavier use of nouns versus verb constructions. Pages with a higher incidence of adjectives may suggest more subjective or promotional content. Detecting the frequency and co-occurrence of POS tags provides useful input to better understand a document’s focus and quality.
Practical examples:
- Flagging pages with an inflated adjective-to-noun ratio could help filter out low-value promotional content.
- Indexing pages based on their distinctive POS tag patterns can aid topical clustering and classification.
- Identifying pages with more past vs present verb constructions may influence assessments of freshness.
Three Potential Actionable SEO Ideas
- Balance page structure for a mix of nouns, verbs, and descriptive phrases suited to the content type.
- Avoid excessive use of qualifiers and intensifiers which may flag subjective promotional content.
- Review site content ensuring consistent verb tenses between product/service pages and related blog posts.
Dependency Parsing
This figures out how each word in a sentence depends on or relates to other words. For example, it links the noun “dog” to the verb “barked”.
Analyzing the syntactic dependency relationships in the sentences of a webpage provides clues about its information content. Recursively identifying which words depend on or modify other words reveals the underlying predicate-argument structure of the text. This allows search engines to glean key semantic aspects, like subjects, objects, and actions described by a document. It also aids in resolving modifiers and qualifiers so that core factual statements can be extracted from the text. Overall, dependency parsing helps search engines move beyond just matching keywords to understanding pages’ meaning and propositional content so that results can better match user intent.
Practical examples:
- Identifying pages with higher densities of object-action-subject dependencies may suggest more informational content.
- Parsing adjective and adverb modifiers may help determine the sentiment expressed toward key topics.
- Linking pronouns and references to their dependencies can unwind long, complex sentences into more meaningful semantic relationships.
Three Potential Actionable SEO Ideas
- Craft page content with clear subjects, objects, and descriptive adjectives around key topics to aid comprehension.
- Use pronoun references to link back to subjects unambiguously for improved readability flow.
- Provide examples of topics tied together in logical sequences with transitional phrases to demonstrate relationships.
Semantics Relations Labeling
This involves tagging how words and concepts relate to each other with role labels. For example, it could link “Steve Jobs” to “Apple” with a “founded” label.
Annotating semantic relationships between entities mentioned in a webpage’s content can augment search engines’ understanding of its topics and meaning. Techniques like semantic role labeling can identify the roles different noun phrases play, like agents, patients, instruments, etc. Extracting subject-predicate-object triples can capture core factual statements made in the text. And labeling relations like “person X founded company Y” can extract precise details about key entities. By parsing out and cataloging these semantic relations, search algorithms can look beyond keyword matching to better model pages’ focus, events described, and reliability.
Practical examples:
- Flagging pages containing contradicting semantic relations could influence rankings by detecting conflicting/inaccurate info.
- Indexing pages’ subject-predicate-object relations enables more semantic, intent-based search.
- Identifying pages with more definitive factual relations may suggest greater authority/accuracy.
Three Potential Actionable SEO Ideas
- For key page entities, specify semantic relationship attributes like creator, user, manager, location to provide additional context.
- Use schema.org structured data to annotate entities, their attributes, and their connections to one another.
- Create an entity-relationship graph visualization on the site illustrating connections between key entities discussed.
Co-occurrence Network Analysis
This studies networks of words that appear together in texts to model connections. For example, it maps out that “pancake” and “syrup” often occur together.
Modeling the networks of co-occurring words and entities on webpages can provide useful clues about their semantic themes and topics. Constructing graphs where nodes are vocabulary and edges show statistical co-occurrence allows search algorithms to map out the key concepts contained within the text. Community detection can identify densely linked nodes that likely correspond to topic clusters. Analyzing the centrality of nodes in the network can uncover dominant themes. Comparing the co-occurrence networks across pages can help segment documents into related groups based on shared concepts. By moving beyond just matching keywords to modeling semantic connections, search engines can better organize and rank pages for relevance.
Practical examples:
- Clustering pages based on similarities in their co-occurrence networks can power more relevant topic-based search.
- Identifying central, high frequency nodes may help distill pages’ primary foci.
- Connecting pages with shared uncommon co-occurring concepts could suggest deeper topical relevance.
Three Potential Actionable SEO Ideas
- Include a topic analysis widget on pages visualizing graphical connections between discussed concepts.
- Create highly interconnected content focused on building out associative chains of related keywords and entities.
- Develop an internal site graph database tracking statistical co-occurrences of terms to inform optimization decisions.
Named Entity Recognition
This labels words in the text that are names of things like people, places, and companies. For example, it identifies “Barack Obama” as a person’s name.
Identifying named entities mentioned on webpages like people, organizations, locations, dates, etc. provides useful signals about page content that go beyond keyword analysis. The prominence and relations of these real-world entities can reveal the focus and provenance of the page text. For example, a high incidence of locations may indicate geo-specific content. Identifying authoritative sources like public figures or experts mentioned could bolster page trustworthiness. Analyzing trends in entity types over time can factor into assessments of freshness as well. By leveraging these entity-based insights, search engines can filter and rank pages in ways that better match entities of interest to the user.
Practical examples:
- Prioritizing pages mentioning sought-after people or organizations caters results to user intent.
- Weighing pages that cite more reputable entities and sources may improve reliability of results.
- Tracking shifts in mentioned entities over time enables filtering results by recency.
Three Potential Actionable SEO Ideas
- For each page, list out key people, organizations, locations, and brands related to the topic to provide a clear entity signal.
- Provide schema-structured data for prominent named entities as @abouts and @mentions.
- Create an entity explorer navigation allowing users to pivot between related entities mentioned across the site.
Semantic Role Labeling
This labels what role words play in a sentence, like “agent” or “theme”. For example, it tags “boy” as the “agent” and “cookie” as the “theme” in “The boy ate the cookie.”
Semantic role labeling involves detecting the roles played by entities within predicates describing events or actions. This provides useful insights for search engines into the nature of the content described on a webpage. For example, identifying agents, patients, instruments, etc. allows search algorithms to categorize pages based on key participants and actions they mention. Distinguishing pages that focus on certain desired roles, like highly relevant agents or patients, enables more intent-based matching. Semantic roles can reveal details about events, like magnitude, duration, frequency, etc. to aid assessments of importance and relevance. Overall, semantic role labeling provides another lens for understanding the meaning of text that can ultimately improve search retrieval and ranking.
Practical examples:
- Prioritizing pages that highlight relevant target participants, like desired agents or patients, caters better to precise user intent.
- Identifying pages detailing high-magnitude events or frequently occurring actions provides signals of importance.
- Semantic role information enables filtering pages to match or exclude particular types of events or actions.
Three Potential Actionable SEO Ideas
- Structure content to clearly identify agents, patients, instruments, etc. related to key actions and events described.
- Annotate content with semantic tags labeling noun phrase roles like <Agent> or <Theme> to support extraction.
- Visually distinguish people, places, and things central to discussed events through typographic highlighting.
EAV Table Analysis
This analyzes tables structured with entities, their attributes, and attribute values. For example, products with prices and sizes in tables or breast cancer (E); Treatments (A); chemotherapy, mastectomy, lumpectomy (V).
For webpages containing structured data in an entity-attribute-value format, analyzing these tables provides shortcuts for search algorithms to directly interpret the page’s content. The entities, their associated descriptive attributes, and the attribute values provide a pre-parsed view of key page information. This avoids the need for more complex NLP techniques to extract facts buried in unstructured text. Analysis of structured data also allows the segmentation of pages based on the types of entities they contain and their related attributes and values. This supports a more granular, intent-based search and ranking of results.
Practical examples:
- Matching sought-after entity types, attributes, and values enables very precise filtering of results.
- Ranking pages based on the relevance of their structured data fits user queries with less ambiguity.
- Analyzing the update frequency of dynamic structured data can inform assessments of page freshness.
Three Potential Actionable SEO Ideas
- Use EAV tables to create master sitemaps and content outlines for topical breadth and depth of coverage.
- Use schema.org terms so search engines can directly interpret descriptions.
- Maintain dynamically updated entity data like glossaries, directories, event listings, etc. in structured databases.
Distributional Semantics
This represents word meaning through statistical patterns of how words occur together. For example, the meaning of “pancake” based on words like “syrup” and “breakfast” that are often nearby.
Modeling the distributional semantics of words on a webpage, based on patterns of co-occurrence across large corpora, provides powerful contextual clues for search algorithms. By representing terms as high-dimensional vectors encoding their meaning, search engines can effectively measure semantic similarity beyond literal keyword matches. Comparing distributional profiles allows search algorithms to connect pages using related concepts, synonyms, analogies, and more. Distributional semantics also enables the categorization of pages based on their conceptual focus. This provides the basis for improved relevance ranking, query expansion, and more intelligent information retrieval overall.
Practical Examples:
- Identifying pages using distributionally similar terms expands matches beyond literal keywords.
- Measuring vector similarity enables nuanced assessments of topical relevance.
- Segmenting pages based on distributional semantics powers better topic-focused search.
Three Potential Actionable SEO Ideas
- Track semantic usage metrics over time to identify emerging trends and model the contextual meaning of important terms.
- Build a usage graph between site terms to refine clusters of related concepts and expand keyword connection pathways.
- Show dynamic word clouds on pages to visualize keyword distributions and conceptual associations.
Meronymy (Part-Whole Relation)
This looks at part-whole relationships between concepts and words. For example, it links “room” as part of “house”.
Analyzing meronymic part-whole relations expressed on webpages can provide search engines with useful hierarchical information about mentioned entities. Identifying pages that describe component parts of larger wholes enables better matching for navigational queries trying to drill down by attributes. For example, a page about “wheel alignment” may be highly relevant for a search seeking information on “auto maintenance”. Modeling part-whole hierarchies also provides overall categorical context about pages’ entities, like a “fin” being part of a “shark”. This allows search engines to leverage inferred connections between pages based on their related place within an ontological hierarchy.
Practical Examples:
- Prioritizing pages focusing on parts of a queried whole aids navigational intent.
- Connecting pages that mention hierarchically related meronyms and holonyms improve relevance.
- Analyzing the balance of parts vs wholes discussed provides clues about page specificity.
Three Potential Actionable SEO Ideas
- Clearly explain parts, components, steps for key wholes like products, services, processes, etc.
- Use structured data itemList markup to detail parts-whole relationships between entities.
- Create visual navigational site maps tying broader wholes to increasingly specific parts.
Super-Subordinate Relation
This looks at broad-narrow relationships between concepts, like “animal” and “dog”. It links general terms to more specific ones.
Identifying super-subordinate relationships between entities referenced on a webpage, including hypernyms, hyponyms and ISA relations, can help search algorithms better organize and categorize page content. Recognizing that a page mentioning “golden retrievers” also relates to “dogs” and “animals” categorizes it into a hierarchical taxonomy. This allows search engines to connect that page to broader, more general queries seeking information about “dogs”, even if that exact term is not present. Analyzing distribution of superordinates and subordinates discussed also provides signals about the contextual specificity of pages. This additional topical and categorical understanding enables improved relevance ranking.
Practical Examples:
- Connecting pages to taxonomically broader concepts expands topical relevance.
- Weighing specificity based on subordinates vs superordinates mentioned improves ranking.
- Leveraging taxonomies aids query understanding and intent disambiguation.
Three Potential Actionable SEO Ideas
- Link site content honing in on specific niches to overview pages focused on the broader category (aka Pillar Pages and supporting articles).
- Utilize Has-a or Is-a hierarchies in site taxonomy, tags, categories, and link structures between pages.
- Include tables detailing taxonomy classes/subclasses related to key entities on topic pages.
Troponymy
This connects different manners of doing something to the action itself. For example, “whispering” and “shouting” are troponyms of “speaking”.
Modeling troponymic relations, which connect manner of action to actions, provides useful contextual understanding for search algorithms. Identifying pages detailing specific “ways” of doing things allows search engines to better match them to queries seeking that type of information at an appropriate level of granularity. For example, pages discussing the troponyms “whispering” or “shouting” may be highly relevant for queries about “talking”. Analyzing troponyms also enables categorization and clustering of pages based on focus on common actions. This can power better recommendation of pages covering different methods and sub-topics around a high-level issue.
Practical Examples:
- Surfacing pages focused on specific manners of queried actions targets user intent.
- Clustering pages discussing related troponyms enables discovery of specialized content.
- Weighing troponym distributions helps assess topical breadth and specificity.
Three Potential Actionable SEO Ideas
- Provide how-to content with steps drilling down on exact methods, integrations, configurations, etc. for target activities.
- Outline different approaches, techniques, mindsets related to goals in tiered structures linking general to increasingly specific.
- Include dynamic filters allowing users to pivot views on a topic—like product uses—by various troponymic facets.
Corpus-Based Analysis
This analyzes statistical language patterns across large collections of texts called corpora. For example, studying word frequencies across newspaper articles.
Analyzing statistical patterns in word usage across entire text corpora provides useful signals for search engines about the focus and meaning of pages. Techniques like distributional semantics modeling, word embedding, and lexical chaining rely on observing relationships between terms across a large body of texts. This enables search algorithms to interpret pages based on usage profiles of terms they contain, even in the absence of obvious keyword matches. Corpus-based analysis also facilitates categorization of pages using semantic similarity measures instead of just surface word matches. Incorporating these corpus insights enables search systems to retrieve and rank pages in ways more aligned with their true relevant meaning.
Practical Examples:
- Corpus-derived word embeddings allow matching pages based on conceptual similarity, not just keywords.
- Identifying lexical chains can link pages that share common themes or entities, even without overlap.
- Clustering pages by corpus-based usage profiles of terms enables better semantic search.
Three Potential Actionable SEO Ideas
- Add links within content to Wikipedia/Wiktionary for disambiguation and grounding meanings from corpus usage.
- Build an internal corpus of reference documents on topics to track how new content compares for keyword usage patterns.
- Show contextual word clouds on pages generated from statistical language models based on corpus analysis.
Lexical Chain Analysis
This finds chains of related words that occur together in text. For example, a sequence like “house”, “room”, “kitchen” relates concepts.
Constructing lexical chains from webpages provides useful insights for search engines into key themes, concepts and relations described by the content. Tracing sequences of semantically related terms strung together throughout the text acts as a summary of important page topics and entities. This enables matching and ranking pages for queries seeking certain concepts or themes, even in absence of exact keyword matches. Length and centrality of lexical chains also helps search engines gauge the prominence and cohesiveness of topics contained within pages. Overall, lexical chain analysis provides a view into semantic essence of pages that goes beyond superficial keyword statistics.
Practical Examples:
- Matching pages containing lexical chains related to query topics improves relevance.
- Assessing chain length and centrality provides signals of topic primacy and content cohesion.
- Linking pages with shared strong lexical chains indicates close semantic relationships.
Three Potential Actionable SEO Ideas
- Structure content using cohesive linking phrases to connect central keywords and concepts throughout the text.
- Design site taxonomy and internal links to create lexical chains linking pages touching related concepts.
- Add contextual tag cloud widgets promoting the serendipitous discovery of pages connected by lexical chaining.
Topic Modeling
This uses statistics to find abstract topics that occur across documents. For example, discovering themes like “cooking” across recipes and blogs.
Applying topic modeling processes like LDA reveals the underlying semantic themes present within webpages based on aggregated word usage patterns. By clustering words and terms into probabilistically derived topics, search engines can overcome reliance on just keyword matching. Instead they can categorize and connect pages based on their composition of latent semantic topics, even without obvious term matches. Analyzing distributions over these learned topics provides insight into the primary foci of pages. Topic modeling thus enables more meaningful relevance ranking, categorization, and recommendation of conceptually related content.
Practical Examples:
- Discovering pages highly weighted towards queried topics improves relevance even without keywords.
- Recommending pages containing similar topic distributions suggests related content interest.
- Organizing pages by their topic distributions enables better discovery by users.
Three Potential Actionable SEO Ideas
- Visualize generated topic models through site page badges, tag clouds, and content recommendations.
- Create topical hubs gathering resources linked across pages strongly associated with target subjects.
- Develop workflows to continuously update topic models as new content gets added to better organize pages.
Cluster Analysis
This groups things like documents or words based on shared attributes using algorithms. For example, clustering articles by topic words they contain.
Applying cluster analysis techniques enables search engines to categorize and group webpages based on shared characteristics. This provides an alternative to traditional keyword-based indexing that relies on surface term matching. Clustering algorithms can leverage various features like word usage, semantics, entities, structure, etc. to detect pages about related concepts. These clusters essentially create topic-based groupings of content, useful for serving users exploratory discovery of subjects related to their queries. Cluster membership also provides additional signals for ranking pages within search results through algorithms like cluster-based retrieval.
Practical Examples:
- Presenting topically cohesive clusters of pages helps users explore niche interests.
- Weighting pages by cluster relevance matches queries with less ambiguity.
- Recommending closely grouped pages satisfies niche information needs
Three Potential Actionable SEO Ideas
- Show topical clusters related to page contents through visualizations or content links for exploratory discovery.
- Use clustering to inform site architecture and internal linking to connect pages around conceptual groups.
- Apply cluster analysis regularly to help focus new content creation on building out strategically meaningful topics.
Association Rule Mining
This finds interesting associations between pieces of information like products purchased together. For example, people who buy cereal often also buy milk.
Mining association rules from webpage content provides insights about commonly co-occurring entities, attributes, and relationships. This detection of patterns like “product X is often purchased with product Y” within web documents enables new relevance signals for search ranking. Pages exhibiting associations strongly tied to user query terms can be prioritized due to implicitly related content. Associations also aid semantic query expansion to surface pages using related terminology. Analyzing evolution of association rules over webpage corpora timelines also informs assessments of freshness and importance.
Practical Examples:
- Prioritizing pages containing queried associations surfaces implicitly relevant content.
- Expanding queries based on related associated rule entities improves recall.
- Tracking trending and disappearing associations helps gauge content urgency.
Three Potential Actionable SEO Ideas
- Structure unique product/service/content bundles informed by association rule mining patterns.
- Highlight related purchases, page views, etc. driven by association rule recommendations.
- Periodically reassess site content adjacencies and links based on emerging associations in usage data.
N-Gram Analysis
This looks at sequences of multiple words together, like pairs (bigrams) or triplets (trigrams). For example, studying the frequency of “strong tea”.
Examining n-gram use and frequencies within webpages provides useful signals about content topics and quality. Uncovering commonly used phrases acts as an additional metric beyond keyword analysis for understanding topical focus. Statistics on n-gram makeup also enable useful comparisons between pages to identify outliers with unusual phrasing. Normalizing metrics like term frequency-inverse document frequency help assess the informative value provided by certain n-grams based on their specificity. Analyzing n-gram evolution over time also provides clues into content freshness and importance of pages.
Practical Examples:
- Matching pages with topical n-grams boosts relevance even in absence of keyword matches.
- Flagging pages with unusual n-gram frequencies helps filter lower quality content.
- Prioritizing pages with more distinctive n-grams rewards unique, specific content.
Three Potential Actionable SEO Ideas
- Research industry specific keywords, phrases, and terminology to naturally integrate explanatory n-grams.
- Use n-gram inclusion as a metric when evaluating content, prioritizing posts with higher densities of topic n-grams.
- Add quotations highlighting relevant key n-gram phrases to help reinforce semantic matching signals.
Collocation Analysis
This finds which words tend to appear together in phrases, like “strong tea”. It looks at these linguistic collocations.
Identifying characteristic collocated words and phrases in webpages provides useful signals about content topics and style. Statistics on common term combinations indicate semantic associations between concepts covered. Comparing collocation patterns between pages can also help segment content based on subject matter through techniques like similarity clustering. Analyzing collocations additionally provides cues about tone and level of formality based on phrasing choices. Unusual or atypical collocations may also indicate lower quality or autogenerated content worth demoting. Overall collocation analysis enables search engines to interpret pages beyond isolated keywords.
Practical Examples:
- Clustering pages by shared collocation patterns enables better topical search and discovery.
- Identifying formal/informal collocations informs stylistic text classifiers to match user preferences.
- Flagging pages with unusual or one-of-a-kind collocations helps filter lower quality content.
Three Potential Actionable SEO Ideas
- Track collocations across new content to identify novel or unusual phrasings needing clarification.
- Query collocation databases to find relevant word partnerships to integrate within copy.
- Implement a site search to retrieve pages containing specific collocated phrases.
Semantic Field Analysis
This studies groups of words related by meaning, like different sports terms. For example, analyzing how words like “soccer”, “goalie”, and “foul” are related.
Examining clusters of related words and concepts present on pages provides a useful view into their themes and topics. Semantic field analysis goes beyond isolated terms to model meaningful groups of related vocabulary. This enables search engines to interpret pages based on which semantic fields they exhibit, even without direct keyword matches. Comparing distribution over fields also allows search algorithms to classify pages by subject matter and genre. Users likewise can browse or filter search results based on semantic field composition matching their interests and goals.
Practical Examples:
- Browsing search results by predominant semantic field facilitates exploratory discovery.
- Weighting pages by semantic field relevance improves topical matching without keywords.
- Comparing field distributions identifies conceptually related pages despite lexical differences.
Three Potential Actionable SEO Ideas
- Organize site sections around semantic fields like industries, product categories, disciplines, etc. with highly related vocabulary on pages.
- Crosslink pages from associated semantic fields to reinforce field connections in site information architecture.
- Prioritize content-expanding edges of semantic fields rather than redundant concepts in the dense core.
Environmental Scanning
This monitors outside information like news, data, and social media relevant to an organization. For example, scanning industry reports and competitors’ tweets.
Monitoring signals from a web page’s broader ecosystem, like social shares, inbound links, and platform interactions, provides useful contextual clues for search ranking beyond just on-page content analysis. Metrics on virality, external validation, and user engagement act as indicators of popularity and trustworthiness. Trends in referrer patterns also give clues about shifting attention and hot topics. Considering these environmental factors in scoring provides a more dynamic, responsive model of relevance compared to static content analysis alone.
Practical Examples:
- Boosting pages with surging social shares or inbound links rewards timely trending content.
- Demoting pages with excessive user bounce rates or short session durations penalizes lower quality or irrelevant content.
- Analyzing referrer patterns informs better ranking for trending topics and locations.
Three Potential Actionable SEO Ideas
- Monitor third-party media sources, social networks, forums, data sets related to site topics to find relevant external linking opportunities.
- Develop analytics dashboards tracking site KPI time series, referral patterns, search/social signals to gain competitive insights.
- Perform routine competitive audits assessing strategy changes like new products, branding, and discounts to identify threats and opportunities.
TF-IDF Analysis
This measures how important a word is by comparing its frequency in a document vs. the collection. For example, “predator” has higher TF-IDF in an article about panthers compared to the term “fur.”
Leveraging statistics like term frequency-inverse document frequency (TF-IDF) provides useful signals for search engines to evaluate the informative significance of words within webpages. TF-IDF measures the relevance of terms based on their frequency within a document compared to the inverse of their prevalence across all documents. This helps surface distinctive keywords strongly associated with a page but rarely seen elsewhere. High TF-IDF terms often indicate the core informative content. Prioritizing pages with higher TF-IDF scores for their keyword matches thus helps retrieve results tightly focused on relevance to the query.
Practical Examples:
- Matching important yet rare query terms with pages featuring high TF-IDF improves relevance.
- Summarizing pages based on high TF-IDF keywords identifies salient topics.
- Comparing TF-IDF keyword distributions enables similarity ranking of pages.
Three Potential Actionable SEO Ideas
- Target integration of technical vocabulary and industry terminology likely to be unique and keywords with high IDF weights.
- Set up Google Alerts for high TF-IDF keywords to prompt creation of content when new relevant documents emerge.
- Evaluate content/page value based on TF-IDF metrics—prioritizing those with distinct meaningful terms versus common words.
Frequency Analysis
This simply looks at how often words or concepts appear. For example, analyzing how many times “elephant” appears on a page about elephants.
Examining the raw term and entity frequencies observed within webpages enables useful insights for search ranking and classification. The degree of repetition of keywords and concepts provides clues about their relative importance to the content. Comparing frequency distributions can help segment pages discussing common vs niche topics based on relative differences. Changes in word usage rates over time also inform assessments of content freshness and attention trends. Overall, straightforward term frequency analysis provides a simple yet effective method for gauging page aboutness.
Practical Examples:
- Prioritizing pages where query terms appear more frequently improves relevance targeting.
- Clustering pages by shared frequency patterns enables topic-based search.
- Identifying rising term frequencies over time highlights trending topics.
Three Potential Actionable SEO Ideas
- Track term frequencies over time to identify rising trends and model the changing importance of keywords.
- Monitor shifts in topical focus based on keyword densities to realign content creation and optimization priorities.
- Direct site search auto-suggestions and recommendations using high-frequency significance keywords.
Semantic Disambiguation
This figures out the intended meaning of ambiguous words based on context. For example, determining if “bat” refers to the animal or baseball bat from context.
Resolving ambiguous words and phrases found on webpages based on intended meaning and context improves search engines’ comprehension. Techniques like word sense disambiguation leverage surrounding semantics to determine appropriate sense. Discovering pages using terms in senses closely matching the user intent enables better relevance matching. Semantic disambiguation also reduces noise from retrieving pages that happen to share ambiguous keywords but exhibit unrelated usage. Machine learning models can additionally leverage disambiguation to reduce dimensionality of semantic features.
Practical Examples:
- Disambiguating query terms limits irrelevant results sharing only ambiguous keywords.
- Detecting specific sense usage helps match nuanced semantic intent.
- Disambiguation enables grouping of pages discussing words in similar senses.
Three Potential Actionable SEO Ideas
- Link ambiguous phrases to Wikipedia pages or provide definitions from dictionaries/glossaries to clarify intended meanings.
- Use structured data like schema.org to tag word senses to assist interpretation.
- Include comparisons highlighting differences when multiple potential senses of words are relevant to avoid confusion.
Stylistic Analysis
This examines attributes like formality, complexity, and tone of writing style. For example, detecting whether the language is professional versus casual.
Examining attributes related to writing style, tone and readability of webpages provides useful signals about content type, quality and target audience. Statistics on vocabulary complexity, sentence structure, formality, and media use classify pages across stylistic dimensions. Search engines can use these linguistic style insights to retrieve results better matching user preferences, like formal registers for scholarly queries versus casual language for pop culture searches. Style also provides proxies for assessing expertise level of pages. And highly atypical styles may indicate autogenerated or copied content of lower quality.
Practical Examples:
- Classifying pages by vocabulary complexity enables targeting results to user expertise.
- Identifying formal/informal tone facilitates matching register to context and intent.
- Flagging pages with unusual style metrics helps filter lower quality content.
Three Potential Actionable SEO Ideas
- Assess site content across style dimensions like formality, complexity, etc. to ensure appropriate register and reading level for the audience.
- Maintain an authoritative, consistent linguistic style across directories, product descriptions, blog posts, etc. to match user expectations.
- Flag outlier pages exhibiting atypical styles to identify potential quality issues or misalignments with target users
Sentiment Analysis
This detects attitudes, opinions, and emotions expressed in text. For example, analyzing whether a movie review is positive or negative.
Detecting sentiment, opinions and attitudes expressed on webpages provides search engines useful contextual understanding beyond just topical facts. The subjective nature of content factors into relevance for many search intents seeking reviews, commentary, or critique. Sentiment analysis also enables filtering results to match a user’s desired affective stance, whether positive endorsement or critical perspectives. Trends in sentiment levels among pages on a topic provide clues about attention cycles and shifting opinions. This additional emotional layer expands search engines’ comprehension of meaning and purpose.
Practical Examples:
- Prioritizing pages matching target sentiment polarity improves relevance for opinion-based queries.
- Summarizing sentiment trends around topics helps users gauge public reactions.
- Connecting pages with aligned sentiment suggests shared perspectives and attitudes.
Three Potential Actionable SEO Ideas
- Direct site search filters to retrieve subsets of reviews expressing positive, negative, or neutral sentiment about offerings.
- Present sentiment data visualizations summarizing opinions about products, services, topics over time.
- Enable users to sort content like FAQs, comments, etc. by sentiment rating to prioritize most useful, positive, and negative feedback.
Pragmatic Analysis
This examines meaning in real-world context, like goals and use cases. For example, studying how language is used to instruct versus just inform.
Modeling real-world context and pragmatics represented in webpage content improves search engines’ ability to match relevance to user situations. Techniques like named entity recognition, knowledge graph integration, and Wikification add concrete entities, events, and background knowledge connected to page topics. This grounds the semantic interpretation in tangible details closer to the end application context. Analyzing pragmatics also aids disambiguation of intents like instructions versus informational queries. Incorporating pragmatics expands systems’ comprehension beyond just abstract language statistics.
Practical Examples:
- Injecting real-world entities and knowledge grounds interpretations in practical context.
- Identifying instructional pages improves capability matching for “how to” searches.
- Linking mentions to knowledge graphs clarifies ambiguous names and terminology.
Three Potential Actionable SEO Ideas
- Contextualize topics through links to explored entities, events, and background information assuming no prior knowledge.
- Supplement content with multi-format media like videos, visualizations, and audio explaining concepts from different perspectives.
- Enable seamless transitions between instructional and reference content to match shifts in user tasks and needs.
Cross-Lingual Analysis
This compares patterns across different languages, like word associations. For example, analyzing how words group together differently in English and Spanish.
Leveraging insights across languages provides additional signals for search engines to connect, categorize and comprehend webpages. Statistical associations between translated vocabulary aid discovery of pages discussing similar topics across languages. Detecting aligned entities and facts in multilingual content, through techniques like cross-lingual entity linking, also help merge signals from different linguistic sources covering the related information. Analyzing divergent term usage and frequencies across languages enables filtering of results by regional relevance. Overall, cross-lingual signals enhance understanding of page content within its cultural and geographic context.
Practical Examples:
- Aligning topics across languages expands viable results for broader geographic relevance.
- Prioritizing pages with higher in-language term frequency improves localization.
- Translating entity mentions enables aggregation of multilingual knowledge signals.
Three Potential Actionable SEO Ideas
- Develop internationalization initiatives expanding content support for non-primary languages aligned with target markets.
- Provide both localized and original versions of content with translations linking equivalent documents.
- Analyze language usage patterns across versions to identify phrases and grammar that are a challenge for localization.
Multimodal Analysis
This looks at text together with images, audio, and video. For example, analyzing articles together with their pictures.
Incorporating features from multimedia elements like images, videos, and structured data coupled with webpage text provides a more complete semantic interpretation. Computer vision techniques identify objects, scenes, and actions in visual media that give additional context to the textual content. Audio analysis extracts speech, tone, and objects like music to add supplemental acoustic signals. Structured data provides direct factual knowledge around page entities and relationships. Composing these disparate modalities enables more detailed modeling of the full information environment conveyed by webpages.
Practical Examples:
- Matching depicted objects, scenes and actions to query topics improves visual relevance.
- Analyzing tone, speech and semantics from video/audio expands comprehension input.
- Structured data interlinks entities and facts from text with knowledge bases.
Three Potential Actionable SEO Ideas
- Expand textual content with relevant imagery, graphics, videos, and audio embedding clues for algorithms to extract.
- Use alt text descriptions for images providing keywords explicitly stating visual concepts and depicted relationships.
- Incorporate structured data like schema.org alongside natural language to connect semantics with machine-interpretable facts.
Discourse Analysis
This studies structure and meaning within dialogues and texts as wholes. For example, looking at how logically ideas flow from sentence to sentence.
Modeling the coherence, cohesion and discourse structure of webpages provides useful insights for assessing topical focus, quality and readability. Analyzing elements like anaphora resolution, lexical chains, entity transitions, syntactic patterns, argument structure and more reveals logical connections tying together a document’s themes. Pages exhibiting disjointed or fragmented discourse likely lack clear thematic focus. Discourse signals also inform text complexity metrics for targeting content to reader levels. Overall, discourse modeling provides a view into how successfully pages convey meaningful, coherent content.
Practical Examples:
- Identifying strong entity thematic chains highlights pages focused on key topics.
- Assessing readability metrics based on discourse complexity enables targeting content difficulty levels.
- Flagging pages with fractured, confusing discourse reduces irrelevant results quality.
Three Potential Actionable SEO Ideas
- Organize content following logical narrative flows using cohesive transitions between introducing, elaborating on, and concluding topics.
- Construct arguments for key theses providing chained premises, evidence, and facts supporting the conclusions.
- Provide contextual scaffolding through glossary definitions, prerequisite explanations, and forward linking to enable coherent comprehension.
Narrative Analysis
This examines stories, plots, and characters. For example, studying roles and arcs for characters in a novel.
For webpages presented as stories or containing narrative elements, analyzing characteristics like plot structure, characters, voices, themes and chronology provides useful contextual signals. Modeling narratives allows search engines to retrieve pages matching desired story attributes, like pages discussing certain character archetypes. Narrative role labeling categorizes page content by functional roles like hero, villain, moral, etc. Sentiment analysis over character mentions provides clues about their portrayal. Identifying setting details aids understanding of context. Overall, narrative modeling facilitates deeper comprehension of story-based page content and its connections to user intent.
Practical Examples:
- Retrieving pages featuring certain character types or narrative roles fits story-based queries.
- Sentiment analysis targeting characters reveals details about their portrayal.
- Identifying chronology and continuity gaps assesses coherent plot structure.
- Recognizing settings provides grounding in geographic and historical context.
Three Potential Actionable SEO Ideas
- Shape content into archetypal story structures around conflicts, characters, resolutions that resonate with innate narrative expectations.
- Tag content with character roles, plot segments, and setting details using schema.org or custom markup.
- Enrich dry statistical reports with anecdotal experiences of impacted people told through narrative arcs.
Historical Analysis
This relates texts to their historical context like eras and events. For example, connecting newspaper articles to the time periods they discuss.
For pages discussing past events, artifacts, people and eras, analyzing temporal signals provides key context for assessing relevance. Techniques like timestamping event mentions, modeling chronological order, and geopolitical entity linking create timelines of historical details described by content. This allows search engines to retrieve pages contextually matching desired time periods, even without direct keyword matches on dates or eras. Connecting pages mentioning aligned historical entities also enables discovery based on shared context. Overall, historical modeling facilitates temporally-aware matching.
Practical Examples:
- Linking event timestamps to query time periods improves historical relevance.
- Identifying anachronisms flags questionable reliability.
- Connecting pages referencing shared eras or iconic figures provides signals of related focus.
- Assessing chronological order helps evaluate narrative continuity and flow.
Three Potential Actionable SEO Ideas
- Contextualize ideas, products, services, and organizations with origin stories highlighting founders and beginnings.
- Tag content with relevant time periods, landmark events, and historical influences using date metadata and links.
- Develop interactive timelines showcasing major milestones and tracing historical progressions.
Geographical Analysis
This identifies location-based patterns like regional terms or addresses. For example, a search query using British English might return different localized results than one done in American English.
Modeling geography-related information on webpages, like locations, distances, boundaries, and geopolitical entities, provides useful contextual understanding for search engines. Techniques like geotagging, geocoding and gazetteer entity linking associate mentions of places with real-world geographic coordinates and regions. This enables location-aware retrieval of pages localized to users’ current or target geographic context. Comparing concentration of geospatial mentions also helps filter pages by regional relevance. Incorporating geography provides grounding that aids relevance matching to locale-specific user needs.
Practical Examples:
- Geotagging locations enables retrieval of pages relevant to users’ current coordinates.
- Weighing pages by density of mentions for a country/city targets localization.
- Linking geospatial entities provides granular coordinates and boundaries.
- Distinguishing domestic vs international orientation filters results by scope.
Three Potential Actionable SEO Ideas
- Identify geographic regions, physical features, and local references allowing pinpointing relevant location-specific content.
- Tag content with geo metadata like coordinates and regions to support mapping of pages to locales.
- Enhance listings and directories with interactive maps enabling location-based searching and filtering.
Cultural Analysis
This relates language to its cultural context like values and traditions. For example, studying how holiday greetings differ between cultures.
Modeling cultural context provides useful signals for search engines to identify pages aligned with users’ societal perspectives and needs. Techniques like analyzing demographics, values, customs, trends, and social institutions mentioned in content enable culturally-aware search. Identifying pages exhibiting user preferences for individualism vs collectivism, power distance, uncertainty avoidance and other cultural dimensions facilitates personalization. Tracking diffusion of cultural concepts like idioms and identities over time also informs assessments of shift and importance. Overall, cultural modeling helps search algorithms select results resonating better with users’ situated cultural worldviews.
Practical Examples:
- Recognizing individualist/collectivist preferences filters pages to match target cultural norms.
- Identifying traditions and rituals highlights pages explaining user celebratory practices.
- Tracking meme spread over time reveals trending cultural ideas and alignments.
- Distinguishing insider vs outsider cultural perspectives targets results to user ethos.
Three Potential Actionable SEO Ideas
- Accommodate cultural nuances through localized content, design elements, and customization balancing universal needs.
- Provide contextual links explaining organization/product histories, values, and traditions shaping unique cultural perspectives.
- Tag content with relevant cultural facets like languages, traditions, attitudes to enable personalized cultural matching.
Ethnographic Analysis
This examines patterns within cultures/communities through language. For example, studying terminology used in medicine across different hospitals.
Examining patterns in topics, relationships, settings and artifacts depicted on webpages enables models of the cultural communities implicitly represented by the content. Network analysis of interactions and roles, conceptual topic extraction, and entity linking uncover social structures and environments associated with pages. Search engines can use these computational ethnographic insights to retrieve pages aligned with particular subcultures or fields of interest to users, even without explicit keywords. Analyzing emergent communities over time also reveals evolving affiliations, values and zeitgeists.
Practical Examples:
- Identifying central entities and relationships models key actors and activities in communities.
- Topical clustering based on vocabulary usage and artifacts models cultural domains.
- Tracking community membership and concept diffusion over time reveals cultural evolution.
- Connecting pages through shared cultural contexts and affiliations enables discovery.
Three Potential Actionable SEO Ideas
- Spotlight diverse contributors and community members in content showcasing equitable inclusion.
- Analyze user research and feedback to identify shared behaviors, values, environments, and rituals within groups.
- Develop personas representing key ethnographic user segments and customize content to their cultural settings.
Accessibility Analysis
This evaluates how accessible content is for people with disabilities. For example, checking if images have text descriptions.
Evaluating webpage accessibility based on inclusive design principles provides useful quality signals for search ranking and Improving accessibility promotes inclusion, enlarges reachable audience, and enhances overall user experience. Techniques like checking color contrast ratios, parsing document structure, and assessing complexity of language quantify the degree of accessibility supported by page design and content. Search engines can factor accessibility scores into relevance rankings to promote pages exhibiting good practices, essential for users with disabilities. Accessibility analysis also gives site owners feedback to guide improvements.
Practical Examples:
- Boosting pages with higher accessibility metrics promotes inclusion.
- Summarizing accessibility audits highlights areas needing improvement.
- Parsing semantic document structure aids comprehension and navigation.
- Simplifying complex language and visuals enhances understandability.
Three Potential Actionable SEO Ideas
- Audit content with automated validation tools to systematically flag issues and prioritize remedies targeting greatest needs.
- Enrich text-based content with video captions, alt text descriptions, audio versions to support disabled users.
- Adapt information architecture and navigation to use landmarks, headings, and focus order enabling non-visual access.
Visual Semiotics Analysis
This interprets visual symbols, signs, and meanings. For example, analyzing what a red traffic light communicates.
Interpreting visual signals on webpages, like images, layout, color, shapes, and videos, provides additional contextual clues for search engines beyond just text. Visual semiotics analysis extracts meanings associated with signs and symbols commonly used in different cultures and contexts. This facilitates topical categorization of pages based on their design elements and image contents. For example, particular graphic symbols strongly associated with a concept may indicate related content without keyword matches. Analyzing alignment of visual signals with text also measures consistency and provides checks for manipulation. Overall, visual semiotics modeling enables richer comprehension of pages.
Practical Examples:
- Recognizing culturally-symbolic visual motifs provides signals about unstated connotations and associations.
- Identifying imagery reinforcing or contradicting text reveals potential inconsistencies or manipulation.
- Clustering pages based on similar compositions of visual symbols enables exploratory discovery.
- Connecting pages featuring alphanumerical signs with consistent meanings improves interpretation.
Three Potential Actionable SEO Ideas
- Select images balancing artistic style with inclusion of obvious visual signifiers and cues reinforcing page themes.
- Add labels, captions, and alt text enumerating notable icons, symbols, objects, colors, compositions depicted in photos/graphics.
- Ensure visual elements stylistically consistent across related pages to strengthen branding, quality, and coherence signals.
Legal and Compliance Analysis
This evaluates how well content follows laws, regulations, and policies. For example checking if privacy policies follow legal requirements.
Evaluating legal, regulatory, and policy implications described on webpages provides useful signals about their reliability, goals, and target users. Reference extraction, entity analysis, and text summarization can identify key laws, rules, norms, and compliance features indicated in page content. Search engines can leverage these insights to retrieve government pages adhering to transparency and ethics regulations when those values are sought. Compliance analysis also enables filtering results appropriate for minors in educational contexts. This facilitates search experiences meeting user needs within societal legal and ethical constraints.
Practical Examples:
- Identifying official policy pages guides users to authoritative sources for regulations.
- Filtering compliant informational content enables safer search for minors.
- Analyzing referenced laws and norms informs evaluations of page biases and misinformation.
- Surfacing conflict of interest and funding disclosures provides transparency clues for assessing page reliability and motivations.
Three Potential Actionable SEO Ideas
- Reference applicable laws, regulations, and standards supporting claims to demonstrate compliance and attention to social responsibility.
- Strengthen privacy policies, terms of service, and disclaimer transparency disclosing practices, risks, and protections to build user trust.
- Audit content for gaps with internal/external compliance guidelines and ethical codes addressing issues like accessibility, advertising, and objectivity.
Psycholinguistic Analysis
This examines the psychology of how language is produced and processed. For example, studying how long it takes people to read certain sentences.
Probing webpage text for psycholinguistic attributes reflective of the author provides useful signals regarding expertise, trustworthiness, and intentions. Deception prediction, reading ease metrics, and stylometry reveal insights about creators perceptible in language patterns. Search engines can thus filter pages based on psycholinguistic profiles indicating desired knowledge-level, frankness and objectivity qualities. Analyzing writing also gives clues about organizational or industry norms authors belong to. Additionally, unusual changes in psycholinguistics may signal impactful external events on authors.
Practical Examples:
- Assessing reading difficulty quantifies expertise levels of content creators.
- Stylometry facilitates grouping pages from common organizational authors.
- Deception prediction identifies manipulative language and underscores need for verification.
- Changes in complexity metrics over time reveal effects of impactful societal events on authors.
Three Potential Actionable SEO Ideas
- Tone content with an approachable style using examples, narratives, active voice reflecting authentic human creators.
- Prioritize jargon-free explanations of complex topics to make expertise accessible to broader audiences.
- Analyze language patterns across pages to ensure consistent personas avoiding confounding mixed identities.
Phonetic Analysis
This studies the sounds and pronunciation patterns of spoken words. For example, analyzing how vowel sounds differ across languages.
Examining the phonetic and phonological patterns in spoken audio and video content associated with webpages provides additional signals for search engines beyond text alone. Speech recognition transcribes spoken words containing phonetic clues about topics, sentiment, accent, origin, and more. Distinct phoneme distributions indicate pronunciation shifts tied to demographics like regional dialects. Analyzing prosodic features in speech like tone, stress, and rhythm conveys emotion and meaning. Phonetics also aid speech normalization for automatic transcription. Overall, phonetic modeling enables richer comprehension from pages’ multimedia.
Practical Examples:
- Recognizing phonetic realizations tied to dialects and accents enables localization of results.
- Detecting prosodic patterns in speech indicates speaker sentiment and disposition.
- Analyzing phoneme statistics classifies regional origins and mother tongues.
- Normalizing pronunciations facilitates multilingual speech transcription at scale.
Three Potential Actionable SEO Ideas
- Produce explanatory videos and podcasts leveraging vocal inflection, emotion, pacing, and other paralinguistic cues alongside speech transcripts.
- Tag audio content with phonetic features like speaker accents, vocal rhythms, and pronunciation clues about origin/language background.
- Adapt speech transcription services integrating custom audio models recognizing niche terms and unique speaking styles.
Morphological Analysis
This looks at the structure and forms of words. For example, studying prefixes like “un-” and suffixes like “-ness”.
Examining the internal structure and morphology of words appearing on webpages provides clues about language conventions, origins, topics, and semantics. Segmenting words into component morphemes like roots and affixes enables better vocabulary understanding and expansion. Identifying common morphological patterns aids search engines in grouping pages from similar language backgrounds. Analyzing morphological complexity also informs text difficulty metrics for result targeting and readability scoring. Overall, morphological modeling facilitates stronger comprehension of pages’ lexical composition and linguistic context.
Practical Examples:
- Segmenting words into morphemes enables query expansion with related term forms.
- Recognizing morphological patterns indicates language background and expertise levels.
- Simplifying complex word forms enhances comprehensibility and aids non-native users.
- Indexing pages by shared morphemes allows discovery across lexical variations.
Three Potential Actionable SEO Ideas
- Link jargon and technical terminology to definitions explaining etymological composition and word origins.
- Foreground sophisticated vocabulary including meaning exposure through constituent morpheme examples and translations.
- Apply morphological segmentation to expand keyword variants for search indexing and recommendation.
Metaphor Analysis
This identifies metaphorical figures of speech and analyzes their meaning. For example, determining what the metaphor “time is money” implies.
Detecting metaphorical expressions in webpage text and interpreting their implied meanings provides search engines additional signals regarding topics and reader experience. Metaphors creatively convey concepts by linking seemingly disconnected semantic domains. Identifying common source and target domains thus reveals abstractions and qualities associated with page contents. Differentiating literal versus figurative language also reduces incorrect parsing and aids text simplification. Furthermore, the prevalence of metaphors acts as a stylistic indicator of descriptive richness and reading complexity.
Practical Examples:
- Recognizing metaphors enables inference of implied characteristics and qualities of subjects.
- Identifying source semantic domains linked to targets reveals conceptual associations.
- Simplifying metaphors into literal equivalents improves comprehensibility for non-native users.
- Weighing metaphor density indicates descriptive flair and cognitive complexity.
Three Potential Actionable SEO Ideas
- Decode and translate figurative language, metaphors, and analogies using plainer expressions to increase accessibility.
- Highlight metaphors in content, linking conceptual domains reflected across language use patterns on the site.
- Build a reference glossary enumerating interpretations for recurring evocative figures of speech needing elucidation.
Pertainymy Analysis (Relational Adjectives)
This looks at relational adjectives that link entities, like “atomic” in “atomic physics”. For example, the pertainym “culinary” relates to cooking.
Analyzing pertainyms, or relational adjectives that characterize types of connections between entities, provides useful signals regarding key relationships and properties described on webpages. Pertainyms compactly encode contextual attributes and semantics. For example, phrases like “atomic physics” and “criminal lawyer” efficiently convey domain associations. Identifying pertinent adjective modifiers enables better comprehension of entity relations mentioned in text. Comparing pertainym co-occurrence patterns also informs similarity assessments between pages based on shared relational phrases. Overall pertains provide a concise lexical lens into semantic associations.
Practical Examples:
- Recognizing domain-specific pertainyms signals key topics and contexts.
- Indexing pages by shared pertains enables discovery across lexical variations.
- Expanding queries with pertinent synonyms matches more subtle semantic relations.
- Assessing the density and centrality of pertainyms identifies core relations and properties.
Three Potential Actionable SEO Ideas
- Supplement nouns with pertinent adjectives characterizing relationships, properties, and attributes that add specifying context.
- Provide a glossary enumerating definitions for niche adjectives tailored to industry/topic semantics.
- Enhance site search with filters or facets using domain-salient pertainyms to refine results by relational attributes.
Synonymy Analysis
This identifies similar words like synonyms. For example, it recognizes “happy” and “glad” as synonyms.
Accounting for synonymous lexical variations on webpages expands search engines’ topical comprehension beyond literal term matching. Recognizing pages using equivalent phrasing through different words counters vocabulary limitation. Analyzing distributions over synsets provides additional signals for categorizing pages by conceptual focus. Connecting documents based on shared synonyms also enables discovery across lexical boundaries. In general, incorporating synonymy facilitates retrieval of relevant pages despite surface linguistic variability.
Practical Examples:
- Expanding queries with synonyms matches pages using equivalent phrasing.
- Clustering results by similar synset patterns improves topical cohesion.
- Recommending synonymous word substitutions improves comprehensibility.
- Comparing pages’ synonym frequency distributions assesses vocabulary richness.
Three Potential Actionable SEO Ideas
- Enrich pages with diverse nuanced phrasings using varied terminology and vocabulary around topics.
- Link synonymous terms and slang/jargon abbreviations to their standard expanded forms to clarify meaning.
- Include a synonym-powered thesaurus search assisting users in navigating alternate phrasings.
Antonymy Analysis
This identifies contrasting word relationships like “hot” vs “cold”. It looks at antonyms.
Accounting for antonymic oppositions and contrasts in the vocabulary of webpages improves search engines’ understanding of topics discussed from multiple perspectives. Identifying antonyms signals presence of competing or conflicting views around concepts. For queries seeking debate and comparison, prioritizing pages exhibiting high antonym density provides more balanced results. Contrastive metrics also help distinguish pages oriented towards positivity/negativity. And tracking changes in antonym usage over time reveals shifting opinions and attention cycles.
Practical Examples:
- Recognizing pages discussing high densities of antonyms indicates presence of debated issues.
- Balancing results showing both extremes of a binary opposite fits exploratory search goals.
- Prioritizing positive or negative antonym polarity matches desired sentiment.
- Monitoring growth in antonym frequency over time reveals rising controversies.
Three Potential Actionable SEO Ideas
- Present contrasting perspectives on issues through linked content pairs spotlighting opposite viewpoints.
- Construct debate, comparison, and pro/con pages weighing both sides of dichotomies using antonymous language.
- Add links between contradictory pages to encourage exploration across opposites spanning a spectrum.
Holonymy Analysis
This identifies whole-part relationships like “car” and “engine”. It analyzes these holonyms.
Modeling holonymic whole-part and whole-substance relations expressed on webpages provides additional hierarchical category knowledge. Identifying meronomies allows search engines to infer connections between pages describing related whole-part concepts, like product features. Holonyms also aid query understanding at different levels of abstraction or composition. Users can pivot results between wholes and parts based on shifting needs. Analyzing distribution across holonym levels further informs page specificity and scope. Overall, holonymy understanding adds nuanced vertical contextualization.
Practical Examples:
- Linking parts to wholes enables pivoting results up or down abstraction hierarchies.
- Recognizing page focus on whole concepts vs component parts indicates scope.
- Querying for parts is inferred to also intend closely related wholes.
- Identifying extensive part-whole hierarchies suggests comprehensive coverage.
Three Potential Actionable SEO Ideas
- Detail parts, components, and steps for processes, products, services, etc. using clearly labeled flow diagrams and hierarchies.
- Tag parts with corresponding whole objects, along with part-whole relationship types like component-of, member-of.
- Enable exploration across part-whole content through site navigation and architecture exposing tree structures.
Causal Analysis
This identifies causal relationships between events and concepts. For example, extracting cause-effect links like “rain causes flooding”.
Detecting and modeling causal relations expressed in webpage text provides useful signals about significant events, influencers, and explanatory connections. Recognizing causal statements enables search engines to better match pages discussing causes or effects of user-specified concepts. Causal chains also highlight impactful entities affecting downstream events. Prioritizing highly causal pages rewards informative explanations over isolated facts. Temporal analysis of causal directionality provides clues about precedent conditions vs outcomes. In general, causal reasoning strengthens search engines’ comprehension of critical relations between real-world events and entities.
Practical Examples:
- Identifying pages rich in causal explanations fits requests for elucidating “why” or “how”.
- Linking causal concepts over time constructs explanatory narratives.
- Recognizing key causal agents and patients reveals significant influencers.
- Distinguishing causes from effects targets desired precedence of events.
Three Potential Actionable SEO Ideas
- Explain key events, discoveries, and phenomena through narratives highlighting driving factors, preconditions, and effects.
- Visually map out causal sequences as flowcharts and timelines linking antecedent causes to resulting effects.
- Tag content with causal relations using vocabulary like leads to, enables, prevents, catalyzes to explicitly encode connections.
Functional Analysis
This examines the functions, purposes, and uses of entities. For example, analyzing the functions of different website features.
Evaluating the functional roles, purposes, and applications described for entities on a webpage provides additional contextual cues for search relevance. For example, identifying key physical functions of objects or procedural goals of activities enables improved matching for intent-oriented queries. Functional knowledge also helps categorize pages based on use cases and practical domains, like tools for gardening. Analyzing user tasks and behaviors associated with functions provides signals about needs and goals. Generally, functional semantics expand comprehension beyond literal topics.
Practical Examples:
- Recognizing an entity’s common applications and uses improves matching for intent-oriented searches.
- Grouping pages by functionality facilitates discovery across domain variations.
- Identifying user tasks and behaviors informs search intent and goals.
- Comparing functional vs decorative roles disambiguates signifying context.
Three Potential Actionable SEO Ideas
- Outline practical applications, uses, and purposes for products, services, tools, and methodologies.
- Supplement product specs with use cases, customer personas, and workflows depicting functional contexts.
- Link to separate pages for each significant functionality detail rather than condensing into a single overwhelming specifications sheet.
Hierarchical Analysis
This identifies hierarchical relationships like rankings and levels. For example, levels like “country > state > county”.
Modeling hierarchical properties, taxonomic structures, and nested categorization relationships expressed on webpages provides additional signals about the specificity, scope, and categorization of content. Identifying hypernymic/hyponymic is-a relations enables inference of pages discussing superclasses and subclasses of query topics. Recognizing hierarchical rankings, levels, and priorities conveys important differentiating ordering. And taxonomic trees aid query understanding and intent disambiguation at various granularities. Overall, hierarchy adds useful dimensional structure.
Practical Examples:
- Linking hypernyms and hyponyms matches general or specialized levels of user intent.
- Recognizing explicit hierarchical rankings assists searches specifying relative levels.
- Taxonomies help categorize pages by navigating tree classifications.
- Identifying boundaries between hierarchical levels improves scoping.
Three Potential Actionable SEO Ideas
- Structure site taxonomy, categories, directories, and link paths to reflect salient categorical hierarchies and rankings.
- Use nested outline formatting with numeric/alphabetic ordering to visually communicate complex hierarchies within content flows.
- Label nodes consistently with hierarchy levels like tier 1/2/3, primary/secondary, etc enable users to navigate up/down structure.
Ontological Analysis
This extracts conceptual models, types, and relationships. For example, analyzing concepts like “professor teaches course” in a university domain model.
Interpreting the ontological concepts, knowledge representations, and semantic abstractions encoded on webpages aids better comprehension of meaning for search engines. Ontology modeling formalizes significant types of entities, their attributes, classifications, and relationships within a domain. Matching these knowledge graphs enables conceptual query understanding. The ontology also provides an abstract vocabulary for comparing and categorizing pages at a higher semantic level. Overall, ontological analysis elevates modeling beyond superficial keywords and topics.
Practical Examples:
- Ontology integration grounds interpretations in formal conceptual models of a domain.
- Classifying pages based on ontology types matches specialized user intents.
- Querying knowledge graphs directly answers requests for entities, attributes and definitions.
- Comparing ontology-based semantics computes conceptual similarity of pages.
Three Potential Actionable SEO Ideas
- Visually diagram key entities, relations, types, attributes, and concepts central to domain knowledge.
- Provide a site legend/glossary linking ontology terms to semantic class definitions within a standardized conceptual framework.
- Tag content with formal ontology element references enabling concept mapping of pages into machine-readable knowledge representations.
Graph Theory Analysis
This represents relationships between entities as network graphs. For example, linking related pages based on shared keywords.
Analyzing webpage content modeled as graphs and networks enables powerful semantic insights through topological techniques. Knowledge graphs convey conceptual relations between entities. Co-occurrence networks capture statistical keyword associations. Hyperlinks show interconnections between documents. Applying graph theory fosters analysis like clique detection, centrality ranking, clustering, and link prediction. These structural inferences supplement traditional NLP, providing signals about meaningful connections, key entities, and community discovery within the linked information network.
Practical Examples:
- Identifying central, highly interconnected nodes pinpoints authoritative pages.
- Community detection in networks reveals pages sharing common concepts and contexts.
- Analyzing cliques and node degree distributions assesses topical diversity.
- Knowledge graph integration directly answers queries about entities and relations.
- Link prediction suggests useful connections between entities and related pages.
Three Potential Actionable SEO Ideas
- Show interactive link graphs highlighting connections between pages based on key entities, topics, and relationships mentioned in content.
- Prioritize highly linked hubs and authorities during content curation as valuable nodes within the site information network.
- Assess content gaps through visual cluster analysis of densely connected pages versus outliers with few connections.
Zero-Shot Learning
This recognizes new concepts without example training data by transferring knowledge. For example, identifying new animal species based on their descriptions.
Zero-shot learning techniques allow search engines to recognize new semantic concepts, intents, and topics on webpages without explicit training examples. Knowledge transfer from known categories enables inference of relevant pages for queries about previously unseen subjects. For instance, word embeddings and semantic graphs can associate new class labels to proximate observed data points. This provides generalized adaptive comprehension to identify pages relevant to emerging or rare search topics with minimal to no direct signals. Zero-shot learning thereby expands search scope to better match obscure user information needs.
Practical Examples:
- Semantic knowledge transfer enables relevance matching for queries about new trending topics.
- Word vector embeddings infer vector proximity to map new classes to observed data clusters.
- Graph-based techniques propagate relevance signals from labeled nodes to closely linked new nodes.
- Generative adversarial networks synthesize pseudo-samples for new categories with just class descriptions.
Three Potential Actionable SEO Ideas
- Supplement pages with related emerging topics inferred from unsupervised embedding space mappings to known content.
- Link categories to higher-level groupings facilitating generalization of labels to encompass new pages.
- Assign pages secondary topical tags to multiply potential relevance signals beyond the most obvious primary subjects.
Anaphora/Coreference Resolution
This links pronouns and references to the entities they refer to. For example, resolving “she” refers to “Emily”.
Resolving anaphoric references and entity coreferences expressed on webpages clarifies key connections and improves comprehension coherence for search algorithms. Identifying the entities aligned to pronouns and abbreviated mentions enables clearer knowledge of significant page topics and their contextual relationships. This facilitates matching user intents seeking pages about specific entity roles and interactions. Improved coherence also aids assessments of topical focus within content. Overall anaphora resolution reduces ambiguity by tying disparate statements together into unified semantics.
Practical Examples:
- Linking pronouns to their referenced named entities improves comprehension accuracy.
- Cross-document coreference provides signals about entities playing central roles.
- Querying for pages referencing target antecedents matches desired entity discussions.
- Assessing density of resolved anaphora provides proxy measurement of coherence.
Three Potential Actionable SEO Ideas
- Edit content replacing ambiguous pronouns with explicit named entity references to improve coherence.
- Tag entity mentions with co-referring alias mentions to tie textual references to unified real-world objects.
- Assess content readability by analyzing the density and resolvability of reference expressions.
Word Sense Disambiguation
This figures out which meaning of a word is used in context. For example, determining if “bank” refers to a financial bank or river bank.
Discovering latent word senses and modeling meaning in context provides search engines more nuanced comprehension of webpage contents. Words can exhibit multiple senses based on usage, so inducting these meanings from data provides better vocabulary understanding compared to just dictionaries. Disambiguating intended sense then reduces inaccurate semantic matching. This enables search algorithms to retrieve results tuned to precise definitions rather than ambiguous keywords. Sense distributions also inform topic clustering. Overall sense induction and disambiguation yields improved lexical semantics.
Practical Examples:
- Discovering data-driven word senses captures emerging meanings and slang.
- Disambiguating senses filter pages using alternate unrelated definitions.
- Clustering pages by aligned sense usage improves topical cohesion.
- Broadening queries with additional inferred senses expands match diversity.
Three Potential Actionable SEO Ideas
- Clarify ambiguous keywords by linking to glossary definitions denoting intended sense and usage context.
- Apply unsupervised word sense induction to group content by discovering meanings beyond predefined dictionary definitions.
- Automatically tag words with likely sense labels based on surrounding context to assist disambiguation.
Word Embedding Analysis
This represents words through numeric vectors encoding semantic meaning. For example, encoding the word “cat” as a list of numbers representing its meaning.
Representing webpage text via dense word embeddings provides search systems expressive semantic comprehension capabilities. Word vectors encoding similarity relations offer nuanced alternatives to exact keyword matching. Search relevance functions can compute vector similarity between query and document terms. Clusters within the embedding space reveal semantic topics and relationships between pages. Trends in vector usage over time indicate changing language. Overall, word embeddings supply rich semantic genome for meaning-based indexing and retrieval.
Practical Examples:
- Embedding similarity scoring matches pages based on conceptual relevance beyond keywords.
- Topical clustering utilizes embedding proximity to group semantically related pages.
- Tracking vector drift over time informs detection of semantic shifts.
- Embedding analogical reasoning resolves analogy queries.
- Outlier embeddings may suggest unusual semantics needing investigation.
Three Potential Actionable SEO Ideas
- Visually plot site pages as projected clusters within a common embedding space for topical analysis.
- Automatically recommend related content based on vector cosine similarities rather than just keywords.
- Regularly retrain word embeddings on new content to update representations with evolving usage.
Taxonomic Analysis
This examines taxonomic classification relationships and hierarchies. For example, analyzing how animals are classified into groups like mammals and reptiles.
Modeling taxonomic classifications and type hierarchies referenced in webpage content provides useful categorization signals. Identifying taxonomies and ontologies with is-a relationships enables search engines to infer connections between pages discussing subclass-superclass entities. Matching user queries to appropriate levels in a taxonomy disambiguates intent granularity. Comparing distribution across taxonomic branches also informs page specificity. Overall, leveraging taxonomies provides an organizing conceptual framework to enhance topical understanding.
Practical Examples:
- Linking pages referencing is-a related classes connects generalized/specialized content.
- Disambiguating query intent based on taxonomic level prevents under/over-generalization.
- Analyzing distribution across taxonomy branches assesses page specificity.
- Suggesting related classes along taxonomic paths aids exploratory discovery.
- Parsing taxonomic classifications enables hierarchical categorical indexing.
Three Potential Actionable SEO Ideas
- Organize site content using a conceptual taxonomy providing a standardized hierarchical classification framework.
- Supplement pages with tables detailing associated parent, child, and sibling taxonomic categories for navigation.
- Tag content with applicable taxonomic classes enabling custom views filtering by subset taxonomies matching user needs.
Path Analysis
This evaluates patterns of connections in sequences and networks. For example, analyzing flows through webpage navigation paths.
Tracing and evaluating paths through networks representing relations between webpage contents provides useful insights. Paths may model sequences like document flows, transitions between entities and topics, hyperlinks, etc. Analyzing path shape, length, convergence, cycles and more reveals patterns within the information network. This facilitates queries about specific connections or flows. Path segmentation also identifies salient subsequences and endpoints. Overall, path-based techniques move beyond individual nodes to model trajectories through page content.
Practical Examples:
- Matching page paths exhibiting sequences aligned to query relations satisfies navigational intent.
- Identifying central nodes and subnets by path intersection pinpoints key content junctions.
- Segmenting paths into meaningful subsequences highlights critical steps and transitions.
- Assessing path diversity distributions informs breadth and redundancy.
- Detecting shortest connecting paths reveals close yet non-obvious relations.
Three Potential Actionable SEO Ideas
- Shape information architecture and internal links to connect related pages into linear prerequisite learning paths or workflows.
- Highlight critical navigational subsequences within longer page content flows using sitemaps or navigation widgets.
- Model user journeys across site to inform optimizations improving completion rates for key tasks and conversions.
Entity Resolution
This identifies when different text strings refer to the same real-world entity. For example, linking “NYC” and “New York City” as the same place.
Identifying equivalent entity mentions across webpages enables connecting information about real-world objects unambiguously. Different pages may reference the same entity using varying surface forms. Entity resolution clusters these lexical variations by the unique underlying entity. This allows aggregation of all relevant pages on a topic even when they lack consistent names or identifiers. More accurately consolidating signals improves ranking quality. It also helps identify authoritative entity representations for disambiguation.
Practical Examples:
- Clustering name, alias, and abbreviation variations links pages about a shared real-world entity.
- Aggregating information across resolved entity references improves ranking accuracy.
- Identifying salient entity representations assists canonicalization for disambiguation.
- Querying for alternate names increases recall and matches implicit intent.
- Improved consolidation of entities enables data deduplication.
Three Potential Actionable SEO Ideas
- Maintain a clean master list of canonical entities with associated name variations, aliases, and abbreviations for consistent usage.
- Apply entity resolution to consolidate user profiles, content metadata, and other data sources with overlapping object references.
- Tag or link ambiguous entity mentions to disambiguated identifiers or knowledge base profiles.
Entity Linking
This links mentions of entities to knowledge bases about them. For example, linking “Barack Obama” to his Wikipedia article.
Linking entity mentions on webpages to knowledge bases provides unambiguous conceptual grounding and expanded contextual understanding. Recognizing real-world entities enables direct integration of factual knowledge. Linking together entities into graphs captures semantic relations and events extracted from the content. This rich network represents key actors, concepts and relationships described by pages. By integrating structured knowledge, search systems move beyond bags-of-words to represent salient entities and relations.
Practical Examples:
- Linking entities to knowledge bases unifies semantic interpretation across data sources.
- Constructing entity relation graphs directly answers semantic queries.
- Recommending related entities using knowledge graph connections enables exploratory discovery.
- Querying entity attributes and facts provides concise and targeted information retrieval.
- Disambiguating entities improves comprehension accuracy.
Three Potential Actionable SEO Ideas
- Enhance entities mentioned in content by linking to associated profiles in knowledge bases like Wikipedia providing additional factual context.
- Visually distinguish linked entities on pages through styling like icons, badges, rich hover previews.
- Enable entity-based site exploration by generating suggestion widgets with related entities extracted from page contents.
Entity Co-Reference Resolution
This identifies multiple expressions that refer to the same entity. For example, linking “Mr. Obama” and “Barack” as referring to the same person.
Identifying multiple expressions and mentions referring to the same entities across webpage contents connects knowledge about real-world objects discussed. Different references to a shared entity strengthen signals about its contextual relevance. Connecting pronouns and abbreviated aliases back to full entity specifications also improves coherence and accuracy. Analyzing concentrations of entity mentions further highlights dominant page topics. Overall, co-reference resolution enhances understanding by tying disparate expressions together into unified semantics about key entities.
Practical Examples:
- Linking all pronoun and alias references to canonical full entity names clarifies object focus.
- Aggregating signals from related co-referring expressions improves relevance recognition.
- Identifying key entities based on high frequencies of co-referenced mentions assists summarization.
- Improved coherence from resolving references aids assessments of page quality.
- Querying for pages mentioning target co-referenced entities matches intent.
Three Potential Actionable SEO Ideas
- Create disambiguation pages detailing specific meanings for high frequency ambiguous entities based on context.
- Implement site search with filters to narrow results by entity roles, relationships, attributes, etc. inferred through co-reference resolution.
- Assess content quality by analyzing density of resolved entity references as a proxy for informational value.
Entity Attribution
This identifies the sources and provenance of entities. For example, analyzing who created a piece of data.
Analyzing statements of attribution, ownership, responsibility and influence associated with entities referenced in webpage text provides useful context. Google uses this in its Experience-Expertise-Authority-Trust quotient. Extracting source relations and provenance conveys important real-world perspectives. For example, identifying influential creators and authors provides authority clues for ranking and credibility. Sentiment expressed towards entities also enables perspective-based clustering. Overall, modeling attribution patterns helps search engines better situate page contents relative to significant sources and stakeholders.
Practical Examples:
- Identifying authoritative creators or authors provides signals of reliability and notability.
- Sentiment analysis towards entities reveals perspectives of influential sources.
- Connecting entities to originating locations/cultures provides contextual grounding.
- Querying for pages referencing given sources targets desired authoritative or subjective views.
- Changes in source attribution over time can reveal shifting opinions and ownership.
Three Potential Actionable SEO Ideas
- Tag entity mentions with source links providing transparency into origin and authoritativeness of claims.
- Visually associate entities with sources through author boxes, inline citations, knowledge panel sidebars, and backlinks.
- Enable attribution-based content filtering by source reliability, political affiliation, company, etc. matching verification preferences.
Entity Classification
This categorizes entities into types and classes. For example, classifying people, places, organizations, etc.
Categorizing entities mentioned on webpages into a taxonomy class hierarchy enables better semantic comprehension for search compared to treating names as just strings. Recognizing class membership provides critical context – location entities behave differently than person entities. Classes also enable inheritance of attributes and relationships from more general types. Overall, entity classification gives structure to extracted knowledge which improves downstream reasoning. Classes additionally allow grouping pages discussing similar types of objects.
Practical Examples:
- Organizing entities into a class hierarchy leverages inherited knowledge applicable to subclasses.
- Disambiguating terms by binding to specific entity classes reduces confusion.
- Clustering pages by distributions over entity classes enables conceptual exploration.
- Prioritizing certain desired entity class matches improves search intent targeting.
- Querying for subclasses leverages inheritance to match specialized instances.
Three Potential Actionable SEO Ideas
- Organize site directories, categories, and filters along a shallow taxonomy classifying niche entity types relevant to users.
- Supplement entities on pages with high-level class abstractions linking tangible objects to conceptual types.
- Generate sidebar widgets recommending related entity content filtered by desired taxonomic classes.
Entity Sentiment Analysis
This identifies the sentiment expressed about entities. For example, detecting positive or negative opinions about products.
Detecting sentiment expressed towards entities referenced in webpage text provides useful signals about opinions and attitudes. Pages containing positive or negative sentiment towards query entities can be prioritized to match desired perspectives. Clustering entities by sentiment patterns also groups perspectives. Comparative sentiment helps gauge controversy and discern disputing viewpoints. Overall, the contextual stances surrounding entities offer insights that complement factual knowledge for search.
Practical Examples:
- Matching pages containing target sentiment polarity about queried entities meets opinion-oriented needs.
- Group entities by aligned/opposed sentiment to model perspectives.
- Track sentiment shifts towards entities over time to reveal changing attitudes.
- Gain nuanced comparative comprehension by modeling sentiment distributions rather than binary labels.
- Identifying disproportionate sentiment indicates potential bias or controversy.
Three Potential Actionable SEO Ideas
- Automatically annotate sentiment polarity towards entities mentioned to distill opinions and stances.
- Visualize sentiment analytics timelines tracking attitude shifts surrounding important people, organizations, products, etc.
- Allow filtering of entity-focused content like reviews, forum posts based on positive/negative/neutral sentiment.
Do we know which of these analysis models Google and Bing are using and to what degree?
No. We do not know which of the language model analyses Google or Bing uses. And just because a patent is filed on a given model, doesn’t mean that model is actually being used in the ranking algorithm. However, from looking at existing patents, we can infer what might be in each of the search engine’s purview. Some of these models might be included in the future or could be in use without a patent existing for that usage The following Google patents relate to specific analysis types above.
Word Embedding Analysis
- Using dense numeric vector representations of words, known as word embeddings, to analyze and compare documents based on the vector similarities and differences. These word vectors encode semantic information about each word based on patterns in a large corpus. (US10192235B1)
Topic Modeling
- Applying statistical topic modeling algorithms like Latent Dirichlet Allocation (LDA) to discover abstract topics and themes that occur across a collection of documents. The models enable grouping and comparing documents based on their composition of latent topics. (US10191932B1)
Semantic Analysis
- Developing semantic networks and knowledge graphs to represent relationships between words, entities, and concepts discussed in documents. Links in the graph enable more complex semantic matching and reasoning. (US10909604B2)
Dependency Parsing
- Analyzing the grammatical dependency relationships between words in sentences to identify how different terms connect and modify one another syntactically. Representing the parse trees enables deeper comprehension. (US10055682B2)
Named Entity Recognition
- Identifying spans of text that represent named entities like people, organizations, and locations as well as numeric entities like times, dates, and money. Tagging these entities provides useful semantic signals. (US10055681B2)
Sentiment Analysis
- Detecting subjective sentiment signals like opinions, attitudes, and emotions expressed in the text of documents. This provides additional contextual clues beyond just factual topic analysis. (US10055683B2)
TF-IDF Analysis
- Calculating term frequency–inverse document frequency statistics to score the relative importance of words in a document compared to a collection. This technique identifies distinctive words that are frequent in a document but rare overall. (US10354200B2) Note: We do not believe Google is using this currently.
Entity Linking
- Linking entity mentions in documents to real-world entities described in a knowledge base or database to ground the concepts in an external catalog of canonical entities. (US10983805B2)
Anaphora/Coreference Resolution
- Identifying pronouns and abbreviated references in text and connecting them to the full entity descriptions they refer back to. This resolves what the abbreviated mentions are pointing to. (US20210192491A1)
Syntactic Analysis
- Analyzing the syntactic composition of sentences based on their underlying grammatical structures and syntax trees. These trees represent how words assemble into larger phrases and clauses. (US11002022B2)
Semantic Parsing
- Transforming natural language text into formal semantic representations capturing underlying predicate-argument relations and logical forms. This encodes sentence meaning in structured data. (US11010700B2)
Giving Credit Where Credit Is Due
I believe in giving credit where it’s due. My friend Ed Baker, whose website can be found at https://www.edwardabaker.com/, compiled this initial list of models and, with Rob Beal, has developed an incredible tool, called EdgeOne Matrix, available as a bonus tool in Schematect designed to aggregate the key advantages produced by each model for a specific keyword, search query, or entity. Ed has granted us access to beta-test the tool’s results, and it’s proving to be fantastic. This experience sparked my curiosity about how each model might be incorporated further into an SEO strategy. Hence, this long article. Our testing has shown that the tool is intriguing and useful for content mapping and creating article outlines. It eliminates the uncertainty in site structures and article outlines by assigning weight to related keywords/entities, based on the presentation of that term in relation to the primary keyword by each of the 63 models. Essentially, the tool identifies the nearest nodes based on the LLM models previously listed.
