Bidirectional Gated Recurrent Unit with Glove Embedding and Attention Mechanism for Movie Review Classification

Abstract

During the current Internet era, enormous volume of structured and unstructured textual data is generated and exchanged online that made the text classification more crucial. With the massive amount of user-generated content on social media platforms, businesses and organizations can leverage sentiment analysis to understand customer opinions and attitudes towards the products, services, or brand. Text classification is a natural language processing task in which a machine learning model is trained to categorize the text into predefined classes or categories. Due to the advent of promising Deep Learning techniques, intelligent and accurate text classification system can be developed. In movie review classification, algorithms are used to automatically categorize movie reviews into positive, negative, or sometimes neutral sentiments based on the opinions expressed in the text. The goal is to automate the process of determining whether a review reflects a positive or negative sentiment, helping users quickly understand the overall reception of a movie. This work proposes an automated movie review classification system based on Bidirectional Gated Recurrent Unit encoder with attention module. This method encodes the words using GloVe word embedding technique. Context information is captured in an efficient manner and the encoding is more reliable. Bidirectional GRU layer captures long-term dependencies in sentences, followed by a custom Attention layer that assigns higher weights to key components. The attention mechanism allows the model to selectively focus on important parts of the input sequence, by dynamically determining attention weights based on task relevance. The model was individually trained and evaluated using popular benchmark IMDb dataset. Experimental studies have proved the better performance of proposed movie review classification system that obtained accuracy of 98%.