Topic Modeling
Search Engines no longer use keywords for ranking content in search results. In 2018, Google confirmed that they had built a topic layer into their knowledge graph, changing the landscape for SEO writers who wish to create relevant content that ranks high in SERP.
Topic modeling uses artificial intelligence to analyze all the content existing on the web for any given topic. From that, the algorithm develops hundreds, in some cases thousands, of subtopics. The most relevant content is identified for each of those subtopics and the topic modeling looks for patterns to understand how these subtopics relate to one another.
Documents aren’t restricted to discussing only one issue. Frequently they address multiple topics. In the case of an article about “topic modeling and SEO,” it will likely employ terms like “search engines,” “optimization,” and “SEO” in addition to those previously mentioned topic model words.
Topic modeling is a requirement for providing fast, relevant results. It’s difficult to envision a way to efficiently produce SERPs without topic modeling. There’s too many pages on the web. The way in which queries are entered is vast and complex.
Topic models reveal latent semantic structures and offer insights into unstructured data, the type of data that pervades the internet. Some popular topic models in natural language processing include LDA (latent Dirichlet allocation ), LSA (latent semantic analysis), and TF-IDF (term frequency-inverse document frequency).
An article, blog post, or other online document about a specific topic will have certain words appearing more frequently than others. For example, an article about topic modeling will frequently mention words such as “model,” “algorithm,” “text,” “data,” and “analysis.”
These terms exhibiting similarities are grouped together and the topic of a specific online document is determined by the topic modeling algorithm based on the statistical probability of occurrence of those words.
Using Topic Modeling
You don’t need to thoroughly understand data science in order to understand the basics of Topic modeling use it effectively as a content creator. Using topics and topic modeling techniques gives you the ability to create relevant content that ranks high in SERP and allows both the algorithm and potential readers to find your content.
Topic clusters, with pillar pages that cover your main topic and supporting pages to cover subtopics and semantically related subjects, give you breadth and depth in a way that’s easily navigated by both humans and search algorithms.
Types of Topic Models
There has been an evolution of chine learning models to create topic models. Here are three examples:
1972: Term Frequency-Inverse Document Frequency
Introduced in 1972, TF-IDF analyzes keyword frequency in a document compared to a set of documents. It measures the number of times a word or combination of words appears in a body of text. Then it determines the degree of relevance the text has to that term by comparing it to a collection of other documents. But its greatest downfall is that it can’t account for relationships, semantics, or syntactics. That’s why it’s not very useful in today’s complex world of SEO.
1988 – Latent Semantic Analysis
Developed in 1988, latent semantic analysis (LSA) looks at the relationship between a set of documents and the terms they contain. Specifically, it produces a set of concepts related to the document and terms. LSA gets us closer to discovering synonyms and semantically related words. But it still can’t identify relationships between topics.
2003 – Latent Dirichlet Allocation
This topic model, created in 2003, is commonly used to identify topical probability and relationships between topic and subtopics. Latent Dirichlet Allocation (LDA) analyzes the connections between words in a corpus of documents. It’s able to cluster words with similar meaning. As a result, you have a more in-depth semantic analysis than earlier topic models.
LDA also utilizes a Bayesian inference model to identify terms related to a topic within a document. It improves those assumptions each time a new document is analyzed. Using LDA, you can get a more precise assessment of the topics discussed in a document.