You have a small dataset and want to build an NLP system for sentiment analysis. Which hybrid approach is best to improve accuracy?

hard📝 Model Choice Q15 of 15

NLP - Sentiment Analysis Advanced

ATrain a deep neural network only, ignoring rules.

BUse handcrafted rules to catch key sentiment words, then train a simple ML model on remaining data.

CUse only handcrafted rules without any machine learning.

DRandomly guess sentiment labels to save time.

Step-by-Step Solution

Solution:

Step 1: Consider dataset size and approach
Small data limits deep learning effectiveness; rules help catch key patterns.
Step 2: Combine rules and ML effectively
Use rules for important sentiment words, then train ML on leftover data for better coverage.
Final Answer:
Use handcrafted rules to catch key sentiment words, then train a simple ML model on remaining data. -> Option B
Quick Check:
Small data + rules + ML = best hybrid [OK]

Quick Trick: Use rules for key words, ML for rest on small data [OK]

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