Sentiment Analysis of Movie Review Using Deep Learning Techniques

Abstract Text mining is a process of identifying meaningful information from text-based data. A large amount of data in the form of reviews and tweets is available on the web. It is difficult to manually read these reviews and assign sentiments to them; therefore, an automated system can be created that analyses the text and extracts user precepts. In this system, sentiment analysis is performed on collected restaurant reviews. The implemented system performs sentiment analysis on the available restaurant review data. This result can provide an opinion that is positive or negative. In the baseline system, we demonstrate two different feature extraction techniques. First, the bag-of-words model is used for feature extraction. The second technique used was the term frequency-inverse document frequency scores. We examined the effectiveness of several N-gram ranges using the naïve Bayes classifier. We analyze the results of the baseline system and the scope of improvement in the results using the word embedding technique. The CNN model efficiently extracts higher level features using convolutional layers and max pooling layers. The LSTM model is capable of capturing long-term dependencies between word sequences. We propose a hybrid model using LSTM and a CNN model, named the Hybrid CNN-LSTM Model, to overcome the sentiment analysis problem. We obtained improved accuracy for sentiment analysis on the IMDB movie review dataset.