Context-Sensitive Sentiment Classification of Short Colloquial Text

Abstract : The wide-spread popularity of online social networks and the resulting availability of data to researchers has enabled the investigation of new research questions, such as the analysis of information diffusion and how individuals are influencing opinion formation in groups. Many of these new questions however require an automatic assessment of the sentiment of user statements, a challenging task further aggravated by the unique communication style used in online social networks.This paper compares the sentiment classification performance of current analyzers against a human-tagged reference corpus, identifies the major challenges for sentiment classification in online social applications and describes a novel hybrid system that achieves higher accuracy in this type of environment.
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Robert Bestak; Lukas Kencl; Li Erran Li; Joerg Widmer; Hao Yin. 11th International Networking Conference (NETWORKING), May 2012, Prague, Czech Republic. Springer, Lecture Notes in Computer Science, LNCS-7289 (Part I), pp.97-108, 2012, NETWORKING 2012. 〈10.1007/978-3-642-30045-5_8〉
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Norbert Blenn, Kassandra Charalampidou, Christian Doerr. Context-Sensitive Sentiment Classification of Short Colloquial Text. Robert Bestak; Lukas Kencl; Li Erran Li; Joerg Widmer; Hao Yin. 11th International Networking Conference (NETWORKING), May 2012, Prague, Czech Republic. Springer, Lecture Notes in Computer Science, LNCS-7289 (Part I), pp.97-108, 2012, NETWORKING 2012. 〈10.1007/978-3-642-30045-5_8〉. 〈hal-01531126〉

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