Evaluation of Features on Sentimental Analysis

Abstract

Sentimental analysis is the method of finding sentiment such as positive or negative from a text data. In this paper we are using some feature selection techniques such as Mutual information, Chi-Square, Information gain and TF-idf to select features from high dimensionality of feature set. These methods are evaluated over the dataset which contains 2000 review data about MOVIES. The classification is performed using support vector machine provided by weka9 tool. We also investigate that which is best feature to extract sentiments from the reviews. We are considering unigram, bigram, POS tags of words and function words as our feature set.