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Why does increasing the number of topics beyond a certain point often reduce the coherence score in topic modeling?

hard📝 Conceptual Q10 of 15
NLP - Topic Modeling
Why does increasing the number of topics beyond a certain point often reduce the coherence score in topic modeling?
ABecause topics become too specific and less meaningful
BBecause the model runs out of words to assign
CBecause the training data size decreases
DBecause the algorithm ignores extra topics
Step-by-Step Solution
Solution:

  1. Step 1: Understand coherence score behavior

    Coherence measures semantic meaningfulness; too many topics split themes too finely.

  2. Step 2: Explain score drop

    Excessive topics create very narrow, less interpretable topics, lowering coherence.

  3. Final Answer:

    Because topics become too specific and less meaningful -> Option A

  4. Quick Check:

    Too many topics = less meaningful topics = lower coherence [OK]
Quick Trick: Too many topics cause less meaningful, specific topics [OK]
Common Mistakes:
MISTAKES
  • Thinking data size or word count causes coherence drop
  • Assuming algorithm ignores extra topics
  • Confusing topic number with training data size

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