A framework for fast-feedback opinion mining on Twitter data streams
Lokmanyathilak Govindan Sankar Selvan, Teng-Sheng Moh · 2015
This paper focuses on the computational infrastructure for fast-feedback opinion mining. This calls for a versatile platform to handle all the possible problems arisen from mining data streams of a social networking site. In particular, we consider the difficulty of getting customer feedbacks faced by companies that produce free software. This is especially challenging since, when encountering buggy software, customers would just switch to another free software with similar functionality without providing any feedback. Our framework makes use of real-time Twitter data stream. These data streams are filtered and analyzed and fast feedback is obtained through opinion mining. The framework is built upon Apache Hadoop to deal with huge volume of data streamed from Twitter. The experiments have shown an 84% accuracy in the sentimental analysis. Our framework is therefore able to provide fast, valuable feedbacks to companies.