Polarizing Sentiments in Movie Reviews Using Improved kNN Classifier

With the flourish of the Web, online review is becoming a more and more useful and important information resource for people. As a result, automatic review mining and summarization has become a hot research topic recently. Different from traditional text summarization, review mining and summarization aims at extracting the features on which the reviewers express their opinions and determining whether the opinions are positive or negative. In this paper, we focus on a specific domain - movie reviews. Sentiment analysis of movie reviews has been done to detect the sentiment behind any review- positive or negative and an improved k- Nearest Neighbor (ImpkNN) classifier has been proposed which deploys the notion of attribute weighted-kNN and the weights associated are trained using 10-fold cross validation. In the end, the outputs of both Basic kNN and ImpkNN are evaluated using graphs.