A Review on Feature Selection Methods for Sentiment Analysis

Text documents are normally represented as a feature-document matrix in sentiment analysis. Features can be single words from the text document or more complex pairs extracted by different schemes that adds information in order to enrich the feature-document matrix representation. Having diverse feature types however creates a problem of high dimensionality due to the vast number of features and relations they hold. Thus, feature selection helps in ensuring that effective and efficient sentiment analysis applications can be developed by selecting features that are relevant and informative to assist classifiers to perform better and to reduce the processing load by narrowing down the feature set. This paper highlights methods used for feature selection, namely filter, wrapper and embedded. Prior to feature selection, preprocessing techniques are performed to reduce the amount of features first. This paper is concluded by summarizing this review and outlining the challenges faced and proposing the ensemble feature selection method for sentiment analysis data.