Visualizing topics (pyLDAvis) in NLP - ML Experiment: Train & Evaluate

Experiment - Visualizing topics (pyLDAvis)

Problem:You have trained a topic model using LDA on a collection of documents. The model shows good training coherence, but you want to better understand and interpret the topics by visualizing them interactively.

Current Metrics:Training coherence score: 0.45; No visualization yet.

Issue:Without visualization, it is hard to interpret the topics and their relationships. The current setup lacks an interactive way to explore topic-word distributions and topic distances.

Your Task

Create an interactive visualization of the trained LDA model topics using pyLDAvis to better understand topic distributions and key terms.

Use the existing trained LDA model and document-term matrix.

Do not retrain the model or change hyperparameters.

Use pyLDAvis for visualization.

Hint 1

Hint 2

Hint 3

Solution

NLP

import pyLDAvis
import pyLDAvis.sklearn

# Assuming lda_model is your trained sklearn LDA model
# and dtm is your document-term matrix (sparse matrix)
# and vectorizer is your CountVectorizer or similar

# Prepare the feature names
feature_names = vectorizer.get_feature_names_out()

# Prepare the visualization data
panel = pyLDAvis.sklearn.prepare(lda_model, dtm, vectorizer, mds='tsne')

# Display the visualization in a Jupyter notebook
pyLDAvis.display(panel)

# Or save to an HTML file
pyLDAvis.save_html(panel, 'lda_visualization.html')

Imported pyLDAvis and pyLDAvis.sklearn for visualization.

Prepared the visualization panel using the trained LDA model, document-term matrix, and vectorizer.

Used t-SNE for better topic distance representation.

Displayed the interactive visualization in notebook or saved as HTML.

Replaced deprecated get_feature_names() with get_feature_names_out()

Passed vectorizer instead of feature_names to pyLDAvis.sklearn.prepare as per latest API

Results Interpretation

Before: Only numeric coherence score (0.45), no visual insight into topics.

After: Interactive visualization showing topic clusters, top words per topic, and topic distances, making interpretation easier.

Visualizing topics with pyLDAvis helps understand the model beyond numbers by showing how topics relate and which words define them, improving interpretability.

Bonus Experiment

Try visualizing topics using pyLDAvis with a gensim LDA model instead of sklearn.

💡 Hint

Use pyLDAvis.gensim.prepare with the gensim LDA model, corpus, and dictionary.