PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Sentiment Knowledge Discovery in Twitter Streaming Data
Albert Bifet and Eibe Frank
In: Discovery Science 2010, October 6-8, 2010, Canberra, Australia.

Abstract

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.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:7197
Deposited By:Albert Bifet
Deposited On:09 March 2011