TANH

Application:

Sentiment Analysis

Imagine you're building a model to analyze product reviews and classify them as "positive," "negative," or "neutral." A key part of this process is an RNN, which is well-suited for processing sequential data like text.

In this scenario, the RNN's job is to read a review word by word and, at each step, update its internal "state" to reflect the sentiment of the words it has processed so far. The TANH function is often used to manage this internal state.

The input to the TANH function would be a weighted sum of the current word's vector representation and the previous state of the network. The output, which is a value between -1 and 1, becomes the new internal state.

• Positive values (closer to 1) would indicate that the network has just processed a word that contributes positively to the overall sentiment (e.g., "amazing," "love," "excellent").
• Negative values (closer to -1) would indicate a word with a negative connotation (e.g., "terrible," "slow," "disappointed").
• Values near 0 would suggest a neutral word or a point where the sentiment is balanced.

This zero-centered range allows the network to effectively represent and carry forward both positive and negative sentiment information, making it easier to determine the overall sentiment of a review.

Example Table

Here's a simplified table demonstrating how the TANH function might process different input values (which represent the combined influence of a word and the previous state) and produce a corresponding output for the next state of the network.