SENTIMENT ANALYSIS OF MOVIE REVIEWS BASED ON FEATURE SELECTION AND RANKING METHOD

Data classification is highly significant in data mining which leads to a number of studies in machine learning with preprocessing and algorithmic technique. Class imbalance is a problem in data classification wherein a class of data will outnumber another data class. Sentiment Analysis is an evaluation of written and spoken language which determines a person’s expressions, sentiments, emotions and attitudes and is commonly used as dataset in machine learning. Twitter is an emerging platform to express the opinion on various issues. Plenty of approaches like machine learning, information retrieval and NLP have been exercised to figure out the sentiment of the tweets. We have used movie reviews as our data set for training as well as testing and merged the naive bayes and adjective analysis for finding the polarity of the ambiguous tweets. Experimental outputs reveal that the overall accuracy of the process is improved using this model. In this work we have focused on two areas like: Feature Selection and Ranking and second using machine learning techniques. We use “Twitter” movie review dataset. We also use accuracy comparison framework for comparing algorithms based on execution time.