RIFT: A Rule Induction Framework for Twitter Sentiment Analysis

The rapid evolution of microblogging and the emergence of sites such as Twitter have propelled online communities to flourish by enabling people to create, share and disseminate free-flowing messages and information globally. The exponential growth of product-based user reviews has become an ever-increasing resource playing a key role in emerging Twitter-based sentiment analysis (SA) techniques and applications to collect and analyse customer trends and reviews. Existing studies on supervised black-box sentiment analysis systems do not provide adequate information, regarding rules as to why a certain review was classified to a class or classification. The accuracy in some ways is less than our personal judgement. To address these shortcomings, alternative approaches, such as supervised white-box classification algorithms, need to be developed to improve the classification of Twitter-based microblogs. The purpose of this study was to develop a supervised white-box microblogging SA system to analyse user reviews on certain products using rough set theory (RST)-based rule induction algorithms. RST classifies microblogging reviews of products into positive, negative, or neutral class using different rules extracted from training decision tables using RST-centric rule induction algorithms. The primary focus of this study is also to perform sentiment classification of microblogs (i.e. also known as tweets) of product reviews using conventional, and RST-based rule induction algorithms. The proposed RST-centric rule induction algorithm, namely Learning from Examples Module version: 2, and LEM2 Corpus-based rules (LEM2 CBR),which is an extension of the traditional LEM2 algorithm, are used. Corpus-based rules are generated from tweets, which are unclassified using other conventional LEM2 algorithm rules. Experimental results show the proposed method, when compared with baseline methods, is excellent, with regard to accuracy, coverage and the number of rules employed. The approach using this method achieves an average accuracy of 92.57% and an average coverage of 100%, with an average number of rules of 19.14.