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NRC-Canada-2014: recent improvements in the sentiment analysis of tweets

From National Research Council Canada

DOIResolve DOI: https://doi.org/10.3115/v1/S14-2077
AuthorSearch for: 1; Search for: 1; Search for: 1
Name affiliation
  1. National Research Council of Canada. Information and Communication Technologies
FormatText, Article
Conference8th International Workshop on Semantic Evaluation (SemEval 2014), August 23-24 2014, Dublin, Ireland
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.
Publication date
PublisherAssociation for Computational Linguistics
In
  • Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014): 443–447.
LanguageEnglish
Peer reviewedYes
NPARC number23001916
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Record identifierf1fa7815-4795-41ab-aabd-c4d5c9c0f8ca
Record created2017-05-24
Record modified2020-04-22
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