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Sentiment Knowledge Discovery in Twitter Streaming Data

  • Albert Bifet
  • Eibe Frank
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6332)

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.

Keywords

Data Stream Application Program Interface Opinion Mining Sentiment Analysis Stochastic Gradient Descent 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Albert Bifet
    • 1
  • Eibe Frank
    • 1
  1. 1.University of WaikatoHamiltonNew Zealand

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