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Why should the number of topics in a topic model be neither too small nor too large?

easy📝 Conceptual Q1 of 15
NLP - Topic Modeling
Why should the number of topics in a topic model be neither too small nor too large?
ABecause a larger number of topics always improves model accuracy
BBecause too few topics oversimplify themes and too many create redundant topics
CBecause the number of topics does not affect the interpretability of the model
DBecause fewer topics always lead to better coherence scores
Step-by-Step Solution
Solution:

  1. Step 1: Understand topic granularity

    Too few topics cause broad, mixed themes that lack detail.

  2. Step 2: Recognize redundancy

    Too many topics often produce overlapping or very similar topics, reducing clarity.

  3. Final Answer:

    Because too few topics oversimplify themes and too many create redundant topics -> Option B

  4. Quick Check:

    Check topic distinctiveness and coverage [OK]
Quick Trick: Balance topic count to avoid oversimplification or redundancy [OK]
Common Mistakes:
MISTAKES
  • Assuming more topics always improve results
  • Ignoring interpretability when choosing topic number

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