| DOI | Resolve DOI: https://doi.org/10.3115/v1/S14-2077 |
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| Author | Search for: Zhu, Xiaodan1; Search for: Kiritchenko, Svetlana1; Search for: Mohammad, Saif1 |
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| Affiliation | - National Research Council of Canada. Information and Communication Technologies
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| Format | Text, Article |
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| Conference | 8th International Workshop on Semantic Evaluation (SemEval 2014), August 23-24 2014, Dublin, Ireland |
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| Abstract | This paper describes state-of-the-art statistical systems for automatic sentiment analysis of tweets. In a Semeval-2014 shared task (Task 9), our submissions obtained highest scores in the term-level sentiment classification subtask on both the 2013 and 2014 tweets test sets. In the message-level sentiment classification task, our submissions obtained highest scores on the Live- Journal blog posts test set, sarcastic tweets test set, and the 2013 SMS test set. These systems build on our SemEval-2013 sentiment analysis systems (Mohammad et al., 2013) which ranked first in both the term- and message-level subtasks in 2013. Key improvements over the 2013 systems are in the handling of negation. We create separate tweet-specific sentiment lexicons for terms in affirmative contexts and in negated contexts. |
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| Publication date | 2014 |
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| Publisher | Association for Computational Linguistics |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| NPARC number | 23001916 |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | f1fa7815-4795-41ab-aabd-c4d5c9c0f8ca |
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| Record created | 2017-05-24 |
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| Record modified | 2020-04-22 |
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