Sentiment Analysis for IMDb Reviews Using Deep Learning Classifier
Sara Sabba, Nahla Chekired, Hana Katab, Nassira Chekkai, Mohammed Chalbi
Abstract
Due to the sheer volume of opinion-rich web resources, a lot of current researches are focusing on sentiment analysis. The goal is to create new models that are able to recognize and classify the opinions or sentiments expressed in an electronic text to evaluate and improve a given system. Many solutions have been proposed to deal with this problem, and most of them are based on Machine Learning techniques. Currently, Deep Learning is a rapidly growing field, it has proven its effectiveness to solve many complex problems due to its ability to learn and extract meaningful information from data. Therefore, many contributions attempt to adopt this approach in the sentiment analysis area such as that of sentiment classification. In this work, we address the problem of user sentiment analysis by proposing a new solution based on natural language processing and deep convolutional neural network. The proposed solution has been tested on the IMDB dataset containing 50,000 movie reviews. The obtained results were very convincing with an accuracy of 99% in the training phase and 89% in the testing phase.