Show simple item record  

dc.contributor.authorBifet, Albert
dc.contributor.authorFrank, Eibe
dc.coverage.spatialConference held at Canberra, Australiaen_NZ
dc.identifier.citationBifet, 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.en_NZ
dc.description.abstractMicro-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.en_NZ
dc.source13th International Conference on Discovery Science (DS)en_NZ
dc.subjectcomputer scienceen_NZ
dc.subjectdata miningen_NZ
dc.subjectMachine learning
dc.titleSentiment knowledge discovery in Twitter streaming dataen_NZ
dc.typeConference Contributionen_NZ
dc.relation.isPartOfProceedings of 13th International Conference on Discovery Science (DS 2010)en_NZ
pubs.volumeLNAI 6332, Lecture Notes in Artificial Intelligenceen_NZ

Files in this item


There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record