10 Questions
What is Gensim?
Gensim is a natural language processing library used for extracting potential topic semantics in documents.
What are some topic model algorithms supported by Gensim?
Some topic model algorithms supported by Gensim are TF-IDF, LSA, LDA, and word2vec.
What is the purpose of evaluating the quality of an LDA topic model?
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.
What are coherence score and perplexity score used for?
Coherence score and perplexity score are used to evaluate the quality of an LDA model and to manually select the number of topics.
What does the coherence score indicate?
The coherence score indicates the clarity of topic quality and the difficulty of understanding topic classification.
What does PyLDAvis visualize in the topic classification process?
The topic distribution of the LDA model under different topic numbers.
What does the circle area represent on the topic distribution map?
The topic's importance in the whole corpus.
What does the distance between one circle center to another represent?
The topic similarity.
How does this study determine the number of research topics?
Based on the coherence score and perplexity score distributions, combined with the distribution of topics under various topic numbers.
What does the coherence score and perplexity score curve distribution help determine?
The topic classification.
Test your knowledge of topic modeling with Gensim library in Python. Learn how to build an LDA topic model and use various algorithms like TF-IDF, LSA, LDA, and word2vec. Explore the process of segmenting the corpus and converting the results into sparse vector sets.
Make Your Own Quizzes and Flashcards
Convert your notes into interactive study material.
Get started for free
