Natural Language Processing in Online Reviews

Abstract

The analysis of the online data posted on various e-commerce sites is required to improve consumer experience and thus enhance global business. The increase in the volume of social media content in the recent years led to the problem of overfitting in review classification. Thus, there arises a need to select relevant features to reduce computational cost and improve classifier performance. This chapter investigates various statistical feature selection methods that are time efficient but result in selection of few redundant features. To overcome this issue, wrapper methods such as sequential feature selection (SFS) and recursive feature elimination (RFE) are employed for selection of optimal feature set. The empirical analysis was conducted on movie review dataset using three different classifiers and the results depict that SVM could achieve f-measure of 96% with only 8% selected features using RFE method.