Feedforward Neural Networks for Sentiment Analysis

Feedforward neural networks are a type of artificial neural network that learns to map input features to outputs through multiple layers of neurons. Here’s how it applies to sentiment analysis:

Overview:

1. Input Layer:
   • Receives the text representation (e.g., bag-of-words or TF-IDF vectors) of the restaurant review.
   • Each word or feature in the representation corresponds to a node in the input layer.
2. Hidden Layers:
   • These layers learn and extract meaningful patterns from the input data.
   • Each hidden layer consists of neurons that are connected to all neurons in the previous layer.
   • Neurons in each layer use activation functions to introduce non-linearity into the model, allowing it to learn complex relationships.
3. Output Layer:
   • The final hidden layer is connected to the output layer.
   • For sentiment analysis, the output layer typically has two nodes: one for positive sentiment and another for negative sentiment.
   • The output values from these nodes represent the probabilities of the review being positive or negative.

Example Illustration:

Suppose we have a restaurant review with a bag-of-words representation:

Input Layer (Bag-of-Words Representation):

• Nodes: [‘delicious’, ‘food’, ‘service’, ‘slow’, ‘disappointing’]

Hidden Layer 1 and 2:

• These layers learn patterns and combinations of input features through their weights and biases.
• They progressively build more abstract representations of the input data.

Output Layer:

• Node 1 (Positive Sentiment): 0.2
• Node 2 (Negative Sentiment): 0.8

In this example, the neural network processes the bag-of-words representation through its layers. The hidden layers learn patterns like how ‘delicious’ and ‘food’ might contribute positively, while ‘slow’ and ‘disappointing’ might contribute negatively. These patterns are combined to make a final sentiment prediction.

Training and Prediction:

• Training: The network adjusts its internal weights and biases during training to minimize the difference between its predictions and actual sentiment labels in the training data. This is achieved using optimization algorithms like gradient descent and a loss function such as cross-entropy loss.
• Prediction: Once trained, the network can predict sentiment for new reviews by passing their text representations through the network and interpreting the output layer’s values.

Extensions and Enhancements:

• Model Complexity: FNNs can be enhanced by adding more layers, using different types of layers (e.g., convolutional or recurrent layers), and incorporating regularization techniques like dropout to prevent overfitting.
• Advanced Techniques: Techniques such as attention mechanisms can also be incorporated to improve the model’s ability to focus on important words or phrases in the reviews.

Importance in NLP:

Feedforward neural networks are foundational in modern NLP systems, including Large Language Models (LLMs). They automate feature learning from text data, making them powerful for tasks like sentiment analysis, text classification, and more complex NLP tasks.

Understanding how FNNs operate helps in effectively applying them to NLP tasks and interpreting their outputs, thereby leveraging their capabilities in various real-world applications.