How can the attention mechanism improve an RNN-based sentiment analysis model to better handle context in complex sentences with mixed sentiments

Question

With the help of code can you tell me How can the attention mechanism improve an RNN-based sentiment analysis model to better handle context in complex sentences with mixed sentiments?

Answer 1

The attention mechanism enhances an RNN-based sentiment analysis model by dynamically weighting important words, allowing it to capture sentiment shifts in complex sentences with mixed sentiments.

Here is the code snippet you can refer to:

In the above code we are using the following key points:

• Uses LSTM for Sequential Context in sentiment analysis.
• Integrates an Attention Mechanism to highlight key words in sentiment shifts.
• Computes Attention Weights from the final hidden state of LSTM.
• Aggregates Important Words dynamically using query-based attention.
• Employs a Dense Layer for sentiment classification into three categories.

Hence, attention mechanisms in an RNN-based sentiment analysis model improve context handling by dynamically emphasizing key sentiment-carrying words, leading to better interpretation of mixed sentiments in complex sentences.