Feature Request: Topic Modeling - Implement Latent Dirichlet Allocation (LDA) and NMF

[HsiangNianian]

Topic modeling is an unsupervised learning task used to discover abstract topics within a collection of documents. We'll implement LDA and NMF algorithms to extract topics from large text corpora.

Algorithm Choice: Should we implement both LDA and NMF for comparison, or focus on one?
Data Handling: How to preprocess the text (e.g., stop words removal, TF-IDF)?
Evaluation: How to evaluate topic coherence and interpretability?

Expected Outcome

• Working implementations of LDA and NMF that can extract topics from a collection of documents.
• Examples and usage guidelines for analyzing text corpora.