Table of Contents
Why add more layers in complex LSTM models for Keras sentiment analysis?
Learn why complex LSTM models in Keras add additional layers for deeper learning, improving hierarchical feature extraction, long‑term dependency modeling, and sentiment classification accuracy with better generalization.
Question
What key improvement is made in the complex LSTM model?
A. Switching from RNN to CNN
B. Fewer hidden units
C. Additional layers for deeper learning
D. Using a different dataset
Answer
C. Additional layers for deeper learning
Explanation
More layers and complexity enhance feature extraction.
Why deeper helps
Stacking LSTM layers increases representational capacity, allowing lower layers to learn local phrase patterns while upper layers capture higher‑level semantics and long‑range dependencies crucial for sentiment cues spread across sentences. Deeper architectures often pair with dropout, recurrent dropout, and possibly bidirectional LSTMs to boost generalization while controlling overfitting.
Option analysis
A is incorrect: Switching to CNN changes the architecture and is not the key improvement described for a complex LSTM model.
B is incorrect: Fewer hidden units reduce capacity and typically hurt performance on nuanced text.
D is incorrect: Using a different dataset does not constitute an architectural improvement to the model.
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