Bifet, A. & Frank, E. (2010). Sentiment knowledge discovery in Twitter streaming data. In B. Pfahringer, G. Holmes, & A. Hoiffmann (Eds.), LNAI 6332, Discovery Science, Proceedings of 13th International Conference, DS 2010, Canberra, Australia, October 6-8, 2010 (pp. 1-15). Berlin, Germany: Springer.
Permanent Research Commons link: http://hdl.handle.net/10289/4864
Micro-blogs are a challenging new source of information for data mining techniques. Twitter is a micro-blogging service built to discover what is happening at any moment in time, anywhere in the world. Twitter messages are short, and generated constantly, and well suited for knowledge discovery using data stream mining. We briefly discuss the challenges that Twitter data streams pose, focusing on classification problems, and then consider these streams for opinion mining and sentiment analysis. To deal with streaming unbalanced classes, we propose a sliding window Kappa statistic for evaluation in time-changing data streams. Using this statistic we perform a study on Twitter data using learning algorithms for data streams.