NLP - Topic Modeling
You run LDA on a set of documents but get topics that mix unrelated words like 'apple' and 'engine' together. What is the most likely cause?
You run LDA on a set of documents but get topics that mix unrelated words like 'apple' and 'engine' together. What is the most likely cause?
medium📝 Debug Q14 of 15
Step-by-Step Solution
Solution:
Step 1: Understand the effect of preprocessing
Without removing stop words and noise, unrelated words can appear together, confusing the model.Step 2: Evaluate other options
Too many topics usually separate words more; sorting word counts does not affect modeling; short documents may reduce quality but not cause mixed unrelated words.Final Answer:
The documents were not preprocessed to remove stop words and noise -> Option AQuick Check:
Preprocessing needed to avoid mixed topics [OK]
Quick Trick: Always preprocess text before topic modeling [OK]
Common Mistakes:
MISTAKES- Blaming topic number without checking preprocessing
- Thinking sorting affects topic quality
- Assuming short documents cause unrelated word mixing
Master "Topic Modeling" in NLP
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