Topic modeling is a central unsupervised machine learning task within semantic analysis. It falls under clustering, meaning it is used to group data points into clusters or identify natural groupings and patterns in data where no predefined labels are provided. Specifically, the goal of topic modeling is to identify underlying themes or topics from large sets of unstructured text, such as blogs, product descriptions, or user feedback, by analyzing patterns in words.
Common algorithms used for this purpose include Latent Dirichlet Allocation (LDA) and BERTopic. The output of these models typically provides information on the probability distribution of topics for a document. Since documents and words can belong to multiple topics (soft or fuzzy clustering approach), topic modeling is well-suited to handle the nuance of natural language.
Topic modeling insights are vital for content strategy, such as identifying key themes (‘Content Marketing’, ‘E-Commerce’) in a content corpus. It can be crucial for improving internal linking by using the identified topics and subtopics to guide links between semantically related articles. It can also be applied to short-form text in a process known as keyword-to-topic mapping, where BERT embeddings group keywords based on semantic similarity to uncover underlying themes or intent in a keyword universe.