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LT3: sentiment analysis of figurative tweets: piece of cake #NotReally

Cynthia Van Hee (UGent) , Els Lefever (UGent) and Veronique Hoste (UGent)
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LT3
Abstract
This paper describes our contribution to the SemEval-2015 Task 11 on sentiment analysis of figurative language in Twitter. We considered two approaches, classification and regression, to provide fine-grained sentiment scores for a set of tweets that are rich in sarcasm, irony and metaphor. To this end, we combined a variety of standard lexical and syntactic features with specific features for capturing figurative content. All experiments were done using supervised learning with LIBSVM. For both runs, our system ranked fourth among fifteen submissions.

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Chicago
Van Hee, Cynthia, Els Lefever, and Veronique Hoste. 2015. “LT3: Sentiment Analysis of Figurative Tweets: Piece of Cake #NotReally.” In SemEval : 9th International Workshop on Semantic Evaluations at Naacl 2015, Proceedings, 684–688. Denver, Colorado: Association for Computational Linguistics.
APA
Van Hee, C., Lefever, E., & Hoste, V. (2015). LT3: sentiment analysis of figurative tweets: piece of cake #NotReally. SemEval : 9th International Workshop on Semantic Evaluations at Naacl 2015, Proceedings (pp. 684–688). Presented at the SemEval : 9th International Workshop on Semantic Evaluations at Naacl 2015, Denver, Colorado: Association for Computational Linguistics.
Vancouver
1.
Van Hee C, Lefever E, Hoste V. LT3: sentiment analysis of figurative tweets: piece of cake #NotReally. SemEval : 9th International Workshop on Semantic Evaluations at Naacl 2015, Proceedings. Denver, Colorado: Association for Computational Linguistics; 2015. p. 684–8.
MLA
Van Hee, Cynthia, Els Lefever, and Veronique Hoste. “LT3: Sentiment Analysis of Figurative Tweets: Piece of Cake #NotReally.” SemEval : 9th International Workshop on Semantic Evaluations at Naacl 2015, Proceedings. Denver, Colorado: Association for Computational Linguistics, 2015. 684–688. Print.
@inproceedings{6827146,
  abstract     = {This paper describes our contribution to the SemEval-2015 Task 11 on sentiment analysis of figurative language in Twitter. We considered two approaches, classification and regression, to provide fine-grained sentiment scores for a set of tweets that are rich in sarcasm, irony and metaphor. To this end, we combined a variety of standard lexical and syntactic features with specific features for capturing figurative content. All experiments were done using supervised learning with LIBSVM. For both runs, our system ranked fourth among fifteen submissions.},
  author       = {Van Hee, Cynthia and Lefever, Els and Hoste, Veronique},
  booktitle    = {SemEval : 9th International Workshop on Semantic Evaluations at Naacl 2015, Proceedings},
  isbn         = {9781941643402},
  language     = {eng},
  location     = {Denver, Colorado},
  pages        = {684--688},
  publisher    = {Association for Computational Linguistics},
  title        = {LT3: sentiment analysis of figurative tweets: piece of cake #NotReally},
  url          = {http://dx.doi.org/10.18653/v1/s15-2115},
  year         = {2015},
}

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