循环神经网络（RNN、LSTM等）能否作为特征提取模块？

Great question—your intuition here is totally on the mark! Let’s break this down specifically for sentiment analysis’s sequence-to-single-output use case:

Yes, recurrent layers (RNN, LSTM, GRU) absolutely function as a feature extraction module that feeds into a feedforward network for classification, mirroring the pattern you see with CNNs. Here’s how it works in practice:

• CNN parallel: For CNNs, convolution/pooling layers extract spatial features from grid-like data (images), then flatten those features and pass them through fully connected feedforward layers to output class probabilities.
• RNN equivalent: For sequential data like text (sentiment analysis), recurrent layers process the sequence step-by-step, building up a contextual representation of the entire input over time. This representation captures temporal dependencies (like how words earlier in a sentence affect the meaning of later words) that are critical for sentiment understanding.

In sentiment analysis specifically, the most common implementations follow this exact pattern:

1. Start with embedded word vectors (converting each word in the sentence to a fixed-size vector).
2. Pass this sequence of embeddings through an LSTM/GRU layer. The recurrent layer outputs a hidden state at every time step—each state encodes the context up to that point in the sequence.
3. Extract a single fixed-size feature vector from the recurrent layer’s outputs:
   • Most often, this is just the final hidden state of the recurrent layer, which distills the entire sequence’s context into one vector.
   • Alternatives include taking the average or maximum of all time-step hidden states (global pooling) to capture broader context without over-reliance on the final token.
4. Feed this feature vector into one or more fully connected feedforward layers (often with dropout for regularization), followed by a softmax activation to output classification probabilities (e.g., positive/negative/neutral sentiment).

This structure is incredibly standard for sequence classification tasks. The recurrent layer’s job is to turn variable-length sequential data into a fixed-size, context-rich feature representation—exactly the "feature extraction" role you’re thinking of—while the feedforward layers handle the final mapping from those features to class labels.

内容的提问来源于stack exchange，提问作者user7641438