Saif, Hassan; Fernández, Miriam and Alani, Harith
(2014).
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| URL: | http://ceur-ws.org/Vol-1272/paper_55.pdf |
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| Google Scholar: | Look up in Google Scholar |
Abstract
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 Unlike most existing approaches, which rely on outdated and context-insensitive stopword lists, our proposed approach considers the contextual semantics and sentiment of words in order to measure their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 |
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| 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 |
| URI: | http://oro.open.ac.uk/id/eprint/41400 |
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