NRC-Canada : building the state-of-the-art in sentiment analysis of tweets

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Proceedings titleProceedings of the Seventh International Workshop on Semantic Evaluation Exercises
ConferenceSemEval-2013, June 2013, Atlanta, GA
AbstractIn this paper, we describe how we created two state-of-the-art SVM classifiers, one to detect the sentiment of messages such as tweets and SMS (message-level task) and one to detect the sentiment of a term within a message (term-level task). Among submissions from 44 teams in a competition, our submissions stood first in both tasks on tweets, obtaining an F-score of 69.02 in the message-level task and 88.93 in the term-level task. We implemented a variety of surface-form, semantic, and sentiment features. We also generated two large word–sentiment association lexicons, one from tweets with sentiment-word hashtags, and one from tweets with emoticons. In the message-level task, the lexicon-based features provided a gain of 5 F-score points over all others. Both of our systems can be replicated using freely available resources.
Publication date
AffiliationNational Research Council Canada; Information and Communication Technologies
Peer reviewedYes
NPARC number21270518
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Record identifier1ba3cbfc-a41f-4b46-a1c4-a30ed0c346b9
Record created2014-02-14
Record modified2016-05-09
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