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Automatic stopword generation using contextual semantics for sentiment analysis of Twitter

Saif, Hassan; Fernández, Miriam and Alani, Harith (2014). Automatic stopword generation using contextual semantics for sentiment analysis of Twitter. In: Proceedings of the ISWC 2014 Posters & Demonstrations Track.

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In this paper we propose a semantic approach to automatically identify and remove stopwords from Twitter data. Unlike most existing approaches, which rely on outdated and context-insensitive stopword lists, our proposed approach considers the contextual semantics and sentiment of words in order to measure their discrimination power. Evaluation results on 6 Twitter datasets show that, removing our semantically identified stopwords from tweets, increases the binary sentiment classification performance over the classic pre-complied stopword list by 0.42% and 0.94% in accuracy and F-measure respectively. Also, our approach reduces the sentiment classifier's feature space by 48.34% and the dataset sparsity by 1.17%, on average, compared to the classic method.

Item Type: Conference or Workshop Item
Copyright Holders: 2014 for the individual papers by the papers' authors.
ISSN: 1613-0073
Extra Information: ISWC-P&D 2014
ISWC 2014 Posters & Demonstrations Track
a track within the 13th International Semantic Web Conference (ISWC 2014)
Edited by Matthew Horridge, Marco Rospocher, Jacco van Ossenbruggen
Keywords: sentiment analysis; contextual semantics; stopwords; Twitter
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Research Group: Centre for Research in Computing (CRC)
Related URLs:
Item ID: 41400
Depositing User: Hassan Saif
Date Deposited: 26 Nov 2014 10:38
Last Modified: 08 May 2019 15:21
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