Comparison of Deep Learning Approaches for Sentiment Classification

Word embeddings are used to convert the unstructured text to numerical values for further analysis. Nowadays, prediction based embedding models like Continuous Bag Of Words (CBOW) and Skip grams are used in comparison to frequency based embeddings. Unlike frequency based embeddings, prediction based embeddings are able to model the semantics of the terms present in a sentence. Sentiment Analysis (SA) is a field of study that aims to automatically extract opinions from the data and to further classify them as positive and negative. The application of sentiment analysis in almost all the domains stands as a motivating factor for this work. It suffers from the problem of non-availability of sufficient labeled data to train the model. Due to the scalability and ability of deep learning models to perform automatic feature extraction from the data, they can be introduced to address this problem. They are also used for various applications due to its capability to extract hierarchical structures from complex data. Keras is a Deep Learning (DL) framework that provides an embedding layer to produce the vector representation of words present in the document. The objective of this work is to analyze the performance of three deep learning models namely Convolutional Neural Network (CNN), Simple Recurrent Neural Network (RNN) and Long Short Term Memory (LSTM) for classifying the book reviews. From the experiments conducted, it is found that LS TM model performs better than CNN and simple RNN for sentiment classification.