Sentiment Analysis: A Survey

Abstract

Traditional approaches to sentiment classification rely on lexical features, syntax-based features or a combination of the two. Word senses used as features show promise, we also examine the possibility of using similarity metrics defined on WordNet to address the problem of not finding a sense in the training corpus. Different methods of sentiment classification are also described in it. Thus is provides a broad way of analyzing the methods and making conclusion provided by these methods. Sentiment Analysis is a Natural Language Processing and Information Extraction task that aims to obtain writer’s feelings expressed in positive or negative comments, by analyzing a large numbers of documents. Generally speaking, sentiment analysis aims to determine the attitude of a speaker or writer with respect to some topic of a document. In recent years, the exponential increase in the Internet usage and exchange of public opinion is the driving force behind Sentiment Analysis today.