Choosing number of topics in NLP - Model Metrics & Evaluation

Metrics & Evaluation - Choosing number of topics

Which metric matters for choosing number of topics and WHY

When picking how many topics to use in a topic model, we want metrics that tell us how clear and useful the topics are. Common metrics include:

• Coherence: Measures how related the top words in each topic are. Higher coherence means topics make more sense together.
• Perplexity: Measures how well the model predicts unseen data. Lower perplexity means better generalization.

Coherence is often preferred because it matches human understanding better. We want a number of topics that balances good coherence without making topics too broad or too narrow.

Confusion matrix or equivalent visualization

Topic modeling does not use a confusion matrix like classification. Instead, we look at metric trends across different topic counts. For example:

Number of Topics | Coherence Score
-----------------|----------------
       5         |      0.42
      10         |      0.51
      15         |      0.48
      20         |      0.44
    

This table shows coherence scores for different topic counts. The best coherence is at 10 topics here.

Precision vs Recall tradeoff (or equivalent)

Instead of precision and recall, topic modeling has a tradeoff between:

• Too few topics: Topics are broad and mix different ideas, making interpretation hard.
• Too many topics: Topics become too specific or noisy, splitting meaningful themes.

Choosing the right number balances clear, distinct topics without losing important themes.

What "good" vs "bad" metric values look like for this use case

Good: Coherence scores around 0.5 or higher usually mean topics are meaningful and interpretable. The number of topics chosen should show a peak or plateau in coherence.

Bad: Very low coherence (e.g., below 0.3) means topics are not related well. Also, if coherence keeps dropping as topics increase, the model may be overfitting or creating noisy topics.

Metrics pitfalls

• Relying only on perplexity: Lower perplexity does not always mean better topics for humans.
• Ignoring interpretability: Metrics can be high but topics may not make sense to people.
• Choosing too many topics: Leads to overfitting and fragmented topics.
• Data leakage: Using test data in training can give misleading metrics.

Self-check question

Your topic model has 25 topics with coherence 0.35 and perplexity improving as topics increase. Is this good?

Answer: No, because coherence is low, topics may not be meaningful. Even if perplexity improves, the topics might be too many and noisy. Consider fewer topics with higher coherence.

Key Result

Coherence score guides choosing number of topics by measuring topic interpretability; aim for highest coherence without too many topics.

Practice

1. Why is it important to choose the right number of topics in topic modeling?

easy

A. To find clear and meaningful groups in the text data

B. To make the model run faster regardless of quality

C. To reduce the size of the text documents

D. To avoid using any stop words in the text

Solution

1. Step 1: Understand the goal of topic modeling

   Topic modeling groups similar words and documents into topics to find hidden themes.

2. Step 2: Importance of topic number choice

   Choosing the right number of topics helps get clear, meaningful groups instead of too broad or too many confusing topics.

3. Final Answer:

   To find clear and meaningful groups in the text data -> Option A

4. Quick Check:

   Right topic number = clear groups [OK]

Hint: Right topic count = clear groups, not too few or many [OK]

Common Mistakes:

• Thinking speed is the main reason to choose topic number
• Believing topic number reduces document size
• Confusing stop words removal with topic number choice

2. Which of the following is the correct way to set the number of topics in a typical LDA model using Python's gensim library?

easy

A. lda_model = LdaModel(corpus, n_topics=5, id2word=dictionary)

B. lda_model = LdaModel(corpus, topics=5, id2word=dictionary)

C. lda_model = LdaModel(corpus, num_topics=5, id2word=dictionary)

D. lda_model = LdaModel(corpus, topic_number=5, id2word=dictionary)

Solution

1. Step 1: Recall gensim LDA parameter names

   The correct parameter to set number of topics is num_topics.

2. Step 2: Check each option

   Only lda_model = LdaModel(corpus, num_topics=5, id2word=dictionary) uses num_topics=5, others use incorrect parameter names.

3. Final Answer:

   lda_model = LdaModel(corpus, num_topics=5, id2word=dictionary) -> Option C

4. Quick Check:

   Parameter name for topics = num_topics [OK]

Hint: Use 'num_topics' parameter to set topic count in gensim LDA [OK]

Common Mistakes:

• Using 'topics' or 'n_topics' instead of 'num_topics'
• Confusing parameter names from other libraries
• Omitting the id2word dictionary parameter

3. Given the following code snippet using sklearn's NMF for topic modeling, what will be the shape of the matrix W if n_components=4 and the input X has shape (100, 500)?

from sklearn.decomposition import NMF
model = NMF(n_components=4, random_state=42)
W = model.fit_transform(X)

medium

A. (4, 4)

B. (4, 500)

C. (100, 500)

D. (100, 4)

Solution

1. Step 1: Understand NMF output matrices

   NMF factorizes X (samples x features) into W (samples x components) and H (components x features).

2. Step 2: Apply shapes to given data

   X shape is (100, 500), n_components=4, so W shape is (100, 4).

3. Final Answer:

   (100, 4) -> Option D

4. Quick Check:

   W shape = samples x components = (100, 4) [OK]

Hint: W shape = number of samples by number of topics/components [OK]

Common Mistakes:

• Confusing W with H matrix shape
• Mixing up rows and columns in matrix shapes
• Assuming output shape equals input shape

4. You ran LDA with num_topics=10 but found many topics have very similar top words. What is the likely issue and how to fix it?

medium

A. Too few topics chosen; increase num_topics to get more variety

B. Too many topics chosen; reduce num_topics to get clearer topics

C. Stop words were not removed; remove stop words to fix

D. The dictionary is too small; add more words to dictionary

Solution

1. Step 1: Analyze similar topics with many overlaps

   If many topics share similar top words, it means topics are not distinct enough, often due to too many topics.

2. Step 2: Adjust number of topics

   Reducing num_topics helps merge similar topics into clearer, distinct groups.

3. Final Answer:

   Too many topics chosen; reduce num_topics to get clearer topics -> Option B

4. Quick Check:

   Similar topics = too many topics [OK]

Hint: Similar topics? Try fewer topics for clarity [OK]

Common Mistakes:

• Increasing topics when topics are already too similar
• Blaming stop words without checking topic overlap
• Adding words to dictionary without checking topic count

5. You have a large collection of news articles and want to find topics. You try 3, 5, 10, and 20 topics. The 3-topic model groups articles too broadly, and the 20-topic model creates many overlapping topics. How should you decide the best number of topics?

hard

A. Choose the number that balances clear, distinct topics without too much overlap, often between 5 and 10

B. Always pick the highest number of topics for more detail

C. Pick the lowest number of topics to keep it simple

D. Randomly select a number since topic modeling is unsupervised

Solution

1. Step 1: Understand the trade-off in topic numbers

   Too few topics cause broad groups; too many cause overlap and confusion.

2. Step 2: Choose a balanced number

   Testing multiple values and selecting one with clear, distinct topics (often between extremes) is best practice.

3. Final Answer:

   Choose the number that balances clear, distinct topics without too much overlap, often between 5 and 10 -> Option A

4. Quick Check:

   Balance topic count for clarity and detail [OK]

Hint: Balance topic count: not too few, not too many [OK]

Common Mistakes:

• Always picking max topics without checking overlap
• Choosing too few topics ignoring broadness
• Ignoring evaluation of topic quality