Topic Modeling

Topic Modeling is an Unsupervised Learning algorithm used for uncovering the underlying themes or topics within a collection of documents, enabling the extraction of meaningful insights and the organization(tagging) of large volumes of textual data.


  • Latent Dirichlet Allocation (LDA): a probabilistic Generative Model often used for Topic Modeling. It assumes that each document is a mixture of topics and each word's presence is attributable to one of the document's topics.
  • Latent Semantic Analysis (LSA)/Latent Semantic Indexing (LSI): They extract underlying topics by analyzing relationships between terms and documents in a high-dimensional space.



  • The goal of Topic Modeling is to discover topics or themes within the Corpus. The topics are not learned in advance.
  • Topic Modeling relies on identifying patterns of words within the corpus.
  • Feature Representation: In topic modeling, documents are typically represented such as Bag of Words (BoW) or TF-IDF.
  • Once the model is trained, it performs Topic Inference using the distribution of topics in each document and the distribution of words in each topic, enabling the identification of the predominant themes in the corpus.
  • Evaluation of topic modeling is often performed using measures such as coherence scores, perplexity, and human judgment, which evaluate the interpretability and coherence of the discovered topics.


Learning Material: