topic-modeler

Topic modeling with LDA uncovers latent themes across a collection of documents by calculating word distributions. It identifies representative keywords for each topic, enabling document summarization and content categorization for large text volumes.

Applying LDA topic modeling to customer reviews identifies recurring themes by extracting representative keywords. Running the script on a CSV text column outputs the top recurring themes and assigns each document to its most probable topic.

Yes, topic modeling uses pandas for data manipulation and matplotlib for generating visual representations of topic distributions. The process also leverages the wordcloud library to generate visual word clouds for each extracted theme.

Execute the topic modeler script on a CSV file by specifying the target text column to analyze. The script extracts the defined number of topics and saves the resulting topic distributions and extracted keywords to a JSON file.

Topic modeling uses the gensim library to perform Latent Dirichlet Allocation for natural language processing. It requires gensim and nltk for text processing to extract latent topics and identify representative keywords from document collections.