Abstract

With the popularization of information and network technology, many emerging and interesting applications have been developed vigorously. The volume and variety of data accumulates rapidly. These data are considered vital assets for supporting crucial business intelligence applications. To better manage and use the valuable data, big data analytics, which is the process of examining large datasets containing a variety of data types to uncover hidden, previously unknown, and potentially useful patterns and knowledge, has become a crucial research issues. In this study, we concentrate on an important big data analytic task, namely opinion mining. We propose a rule-based opinion sentence identification (R-OSI) technique, which can retrieve relevant review sentences to a specific product feature of interest from a large volume of consumer reviews. The novelty of the proposed technique is that it adopts a semi supervised learning approach by requesting a user to provide keywords to describe the target product feature. In addition, a set of unannotated consumer reviews are retrieved from various social media websites. On the basis of the user-provided keywords and the set of unannotated consumer reviews, the class association rule mining algorithm is applied to learn a set of opinion sentence identification rules for the target product feature. Our empirical evaluation results suggest that the proposed R-OSI technique achieves promising performance in opinion sentence identification, even when a supervised learning approach is adopted as the performance benchmark.  

Keywords

opinion sentence identificationopinion mininguser generated contentsocial media analyticsbig data analytics