Can We Solve Sentiment Analysis with ANN, or Do We Need to Transition to?RNN?

Sentiment analysis involves determining the sentiment of textual data, such as classifying whether a review is positive or negative. At first glance, Artificial Neural Networks (ANN) seem capable of tackling this problem. However, given the sequential nature of text data, RNNs (Recurrent Neural Networks) are often a more suitable choice. Let’s explore this in detail, supported by visual aids.

Sentiment Analysis Problem?Setup

We consider a dataset with sentences labelled with sentiments:

Preprocessing the Text?Data

1. Tokenization: Splitting sentences into words.
2. Vectorization: Using techniques like bag-of-words or TF-IDF to convert text into fixed-size numerical representations.

Example: Bag-of-Words Representation

Given the vocabulary: ["food", "good", "bad", "not"], each sentence can be represented as:

• Sentence 1: [1, 1, 0, 0]
• Sentence 2: [1, 0, 1, 0]
• Sentence 3: [1, 1, 0, 1]

Attempting Sentiment Analysis with?ANN

The diagram below represents how an ANN handles the sentiment analysis problem.

• Input Layer: Vectorized representation of text.
• Hidden Layers: Dense layers with activation functions.
• Output Layer: A single neuron with sigmoid activation, predicting sentiment.

Issues with ANN for Sequential Data

1. Loss of Sequence Information:

• ANN treats input as a flat vector, ignoring the word order.
• For example, “The food is not good” is indistinguishable from “The good not food.”

2. Simultaneous Input:

• All words are processed together, failing to capture dependencies between words.

Transition to?RNN

Recurrent Neural Networks address the limitations of ANNs by processing one word at a time and retaining context through hidden states.

• Input Layer: Words are input sequentially (e.g., “The” → “food” → “is” → “good”).
• Hidden Layers: Context from previous words is retained using feedback loops.
• Output Layer: Predicts sentiment after processing the entire sentence.

Comparing ANN and RNN for Sentiment Analysis

While ANNs can solve simple text classification tasks, they fall short when dealing with sequential data like text. RNNs are designed to handle sequences, making them the ideal choice for sentiment analysis and similar tasks where word order and context are crucial.

By leveraging RNNs, we ensure that the model processes and understands text in a way that mimics human comprehension. The feedback loop and sequential processing of RNNs make them indispensable for modern NLP tasks.

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