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A domain-specific sentiment model returns errors when processing new texts. Which fix is most appropriate?

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NLP - Sentiment Analysis Advanced
A domain-specific sentiment model returns errors when processing new texts. Which fix is most appropriate?
AAdd a default sentiment score for unknown words
BRemove all punctuation from training data
CTrain the model only on positive examples
DUse a smaller vocabulary size
Step-by-Step Solution
Solution:

  1. Step 1: Identify cause of errors

    Errors often occur when unknown words have no assigned sentiment score.

  2. Step 2: Implement default handling

    Assigning a default score (e.g., zero) for unknown words prevents errors during prediction.

  3. Final Answer:

    Add a default sentiment score for unknown words -> Option A

  4. Quick Check:

    Default scores prevent unknown word errors [OK]
Quick Trick: Use default scores for unknown words [OK]
Common Mistakes:
MISTAKES
  • Ignoring unknown words
  • Training on only positives
  • Reducing vocabulary unnecessarily

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