Sentiment Analysis Using Recurrent Neural Networks

Introduction

👨‍💻This project implements a sentiment analysis model for movie reviews using RNNs built with Python's libraries.
🔭 Sentiment analysis, also known as opinion mining, is a subfield of Natural Language Processing (NLP) that aims to automatically detect the emotional tone of a piece of text.
⚡This project demonstrates proficiency in deep learning, data manipulation, text preprocessing, NLP, model building, evaluation and handling sequential data.

After loading the data, perform preprocessing techniques like HTML tag removal, punctuation removal, lowercasing, stop word removal using libraries nltk, re and spacy.
Make Tokens for the preprocessed text using Tokenizer. The Tokenizer class from Keras tokenizes the text, converting words into sequences of integer indices.
Sequences are padded with zeros to ensure a fixed length for all reviews using pad_sequences.
Sentiment labels ("positive" and "negative") are converted to numerical labels (0 and 1) using LabelEncoder.

Complex Bidirectional LSTM-GRU has been used in model building. This is a more complex model with multiple stacked Bidirectional LSTM and GRU layers with dropout regularization. Total trainable parameters were 37379651.
The data is split into training and testing sets using train_test_split.
Each model is compiled with the Adam optimizer, binary cross-entropy loss function, and accuracy metric.
Models are trained for a specified number of epochs and batch size.
Training and validation accuracy and loss are monitored during training.

The code demonstrates how to use the trained model to predict the sentiment of new movie reviews.
Text pre-processing steps are applied to the new review text given as input.
The preprocessed text is converted to a sequence of integers using the fitted tokenizer.
The sequence is padded to match the maximum review length.
The model predicts the sentiment probability (positive or negative) for the review.
Based on the predicted probability, the review is classified as "flop movie", "average movie", "good movie", or "blockbuster movie".
Overall this this code provides a basic framework for sentiment analysis using RNNs. You can experiment with different RNN architectures, hyperparameters.

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