Topic Modeling in Python
In this topic modeling project-based tutorial, I have gone through the following steps:
In this project, I have defied a function perform_topic_modeling that takes the number of topics, documents path, and output CSV path as arguments. It then:
1. Loads the documents(Generating sample documents)
2. Preprocesses the text by removing stop words and stemming words.
3. Creates a TF-IDF vector representation of the documents.
4. Performs LDA topic modeling with the specified number of topics.
5. Extracts the document-topic weight matrix.
6. Prepares the data for CSV format, including document IDs and topic weights.
7. Saves the results to the specified CSV file.
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