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Sentiment Knowledge Discovery in Twitter Streaming Data
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BibTeX
@MISC{Bifet_sentimentknowledge,
author = {Albert Bifet and Eibe Frank},
title = {Sentiment Knowledge Discovery in Twitter Streaming Data},
year = {}
}
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Abstract
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. 1
Keyphrases
sentiment knowledge discovery twitter streaming data data mining technique classification problem twitter data stream knowledge discovery new source twitter data unbalanced class data stream mining sentiment analysis data stream time-changing data stream twitter message micro-blogging service sliding window kappa statistic opinion mining
