SentiME++ at SemEval-2017 Task 4: Stacking State-of-the-Art Classifiers to Enhance Sentiment Classification

Raphaël Troncy, Enrico Palumbo, Efstratios Sygkounas, Giuseppe Rizzo


Abstract
In this paper, we describe the participation of the SentiME++ system to the SemEval 2017 Task 4A “Sentiment Analysis in Twitter” that aims to classify whether English tweets are of positive, neutral or negative sentiment. SentiME++ is an ensemble approach to sentiment analysis that leverages stacked generalization to automatically combine the predictions of five state-of-the-art sentiment classifiers. SentiME++ achieved officially 61.30% F1-score, ranking 12th out of 38 participants.
Anthology ID:
S17-2107
Volume:
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
Month:
August
Year:
2017
Address:
Vancouver, Canada
Venue:
SemEval
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
648–652
Language:
URL:
https://aclanthology.org/S17-2107
DOI:
10.18653/v1/S17-2107
Bibkey:
Cite (ACL):
Raphaël Troncy, Enrico Palumbo, Efstratios Sygkounas, and Giuseppe Rizzo. 2017. SentiME++ at SemEval-2017 Task 4: Stacking State-of-the-Art Classifiers to Enhance Sentiment Classification. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 648–652, Vancouver, Canada. Association for Computational Linguistics.
Cite (Informal):
SentiME++ at SemEval-2017 Task 4: Stacking State-of-the-Art Classifiers to Enhance Sentiment Classification (Troncy et al., SemEval 2017)
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PDF:
https://aclanthology.org/S17-2107.pdf