Comparative Sentiment Analysis on a Set of Movie Reviews Using Deep Learning Approach

This paper provides an insight to one of the recent additions in the turf of Machine Learning culture - the process of learning representation or features, known as Deep Learning. It is highly anticipated that Deep Learning will fare much better than the traditional machine learning algorithms not only because of scalability but also of its ability to perform automatic feature extraction from raw data. This paper deals with the analyzing of sentiments on a set of movie reviews, which is considered to be the most demanding facet of NLP's. In this paper, Google's algorithm Word2Vec has been applied on a large movie review dataset to classify text so that the semantic associations between the terms stay conserved. A comparative study of the performances of some notable clustering algorithms is demonstrated concerning their application involving a variable number of features and classifier types as well as variable number of clusters.