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.
-----------------------------------------------------------------------------------
💰Donate to us at https://donorbox.org/donate-epythonlab
Join this channel to get exclusive access:
https://bit.ly/363MzLo
----------------------------------------------------------------------------------
Join the discussion groups:
💻 Telegram: https://epythonlab.t.me/
💻 Facebook: / epythonlab1
💻 Twitter: / epythonlab1
-----------------------------------------------------------------------------------
COME AGAIN!
-----------------------------------------------------------------------------------
On this page of the site you can watch the video online Topic Modeling in Python with a duration of hours minute second in good quality, which was uploaded by the user Epython Lab 01 January 1970, share the link with friends and acquaintances, this video has already been watched 238 times on youtube and it was liked by 9 viewers. Enjoy your viewing!
