Semi-supervised Aspect Based Sentiment Analysis for Movies Using Review Filtering

Abstract

Aspect based Sentiment Analysis (ABSA) is a subarea of opinion mining which enables one to gain deeper insights into the features of items which interest the users by mining reviews. In this paper we attempt to perform ABSA on movie review data. Unlike other domains such as camera, laptops restaurants etc, a major chunk of movie reviews is devoted to describing the plot and contains no information about user interests. The presence of such narrative content may potentially mislead the review analysis. The contribution of this paper is two-fold: a two class classification scheme for plots and reviews without the need for labeled data is proposed. The overhead of constructing manually labeled data to build the classifier is avoided and the resulting classifier is shown to be effective using a small manually built test set. Secondly we propose a scheme to detect aspects and the corresponding opinions using a set of hand crafted rules and aspect clue words. Three schemes for selection of aspect clue words are explored - manual labeling (M), clustering(C) and review guided clustering (RC). The aspect and sentiment detection using all the three schemes is empirically evaluated against a manually constructed test set. The experiments establish the effectiveness of manual labeling over cluster based approaches but among the cluster based approaches, the ones utilizing the review guided clue words performed better.