Sentiment analysis from movie reviews using LSTMs

Abstract

With the advent of social networking and internet, it is very common for the people to share their reviews or feedback on the products they purchase or on the services they make use of or sharing their opinions on an event. These reviews could be useful for the others if analyzed properly. But analyzing the enormous textual information manually is impossible and automation is required. The objective of sentiment analysis is to determine whether the reviews or opinions given by the people give a positive sentiment or a negative sentiment. This has to be predicted based on the given textual information in the form of reviews or ratings. Earlier linear regression and SVM based models are used for this task but the introduction of deep neural networks has displaced all the classical methods and achieved greater success for the problem of automatically generating sentiment analysis information from textual descriptions. Most recent progress in this problem has been achieved through employing recurrent neural networks (RNNs) for this task. Though RNNs are able to give state of the art performance for the tasks like machine translation, caption generation and language modeling, they suffer from the vanishing or exploding gradients problems when used with long sentences. In this paper we use LSTMs, a variant of RNNs to predict the sentiment analysis for the task of movie review analysis. LSTMs are good in modeling very long sequence data. The problem is posed as a binary classification task where the review can be either positive or negative. Sentence vectorization methods are used to deal with the variability of the sentence length. In this paper we try to investigate the impact of hyper parameters like dropout, number of layers, activation functions. We have analyzed the performance of the model with different neural network configurations and reported their performance with respect to each configuration. IMDB bench mark dataset is used for the experimental studies.