Mastering Topic Modeling with Gensim in Python

Gensim is a natural language processing library used for extracting potential topic semantics in documents.

Some topic model algorithms supported by Gensim are TF-IDF, LSA, LDA, and word2vec.

The purpose of evaluating the quality of an LDA topic model is to judge the improvement ability of the parameters and select the optimal number of topics.

<p>Coherence score and perplexity score are used to evaluate the quality of an LDA model and to manually select the number of topics.</p> Signup and view all the answers

<p>The coherence score indicates the clarity of topic quality and the difficulty of understanding topic classification.</p> Signup and view all the answers

<p>The topic distribution of the LDA model under different topic numbers.</p> Signup and view all the answers

<p>The topic's importance in the whole corpus.</p> Signup and view all the answers

<p>The topic similarity.</p> Signup and view all the answers

<p>Based on the coherence score and perplexity score distributions, combined with the distribution of topics under various topic numbers.</p> Signup and view all the answers

<p>The topic classification.</p> Signup and view all the answers