Yeah Latent Dirichlet Allocation (LDA) looks well-structured. However, it seems like its trying to read a CSV file named ‘[login to view URL]’, which have not been provided.
Just some suggestions for potential improvements and considerations:
Language Support: If the dataset contains text in languages other than English, consider adding additional stop words for those languages. NLTK provides stop words for several languages which can be helpful.
Text Normalization: In addition to lemmatization, you might also want to consider stemming, which can sometimes provide a more robust normalization, although it’s more aggressive than lemmatization.
Hyperparameter Tuning: The parameters in the LDA model, such as the number of topics (num_topics) and the number of passes (passes), can significantly affect the results. You may want to experiment with these parameters to find the best model for your data.
Topic Coherence: After training your LDA model, consider evaluating it using topic coherence measures like UMass or c_v coherence. This will help understand the quality of the topics generated by your model.
Visualization: Tools like pyLDAvis offer great ways to visualize the topics and their distributions, making it easier to interpret the results of the LDA model.
[ Other than that its great work, I need it today
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I also need a report with attached format
Project ID: #37659592
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