Sentiment analysis of movie reviews: A study on feature selection classification algorithms

摘要

Sentiment analysis is a sub-domain of opinion mining where the analysis is focused on the extraction of emotions and opinions of the people towards a particular topic from a structured, semi-structured or unstructured textual data. In this paper, we try to focus our task of sentiment analysis on IMDB movie review database. We examine the sentiment expression to classify the polarity of the movie review on a scale of 0(highly disliked) to 4(highly liked) and perform feature extraction and ranking and use these features to train our multi-label classifier to classify the movie review into its correct label. Due to lack of strong grammatical structures in movie reviews which follow the informal jargon, an approach based on structured N-grams has been followed. In addition, a comparative study on different classification approaches has been performed to determine the most suitable classifier to suit our problem domain. We conclude that our proposed approach to sentiment classification supplements the existing rating movie rating systems used across the web and will serve as base to future researches in this domain. "Our approach using classification techniques has the best accuracy of 88.95%".