INGEOTEC at SemEval 2017 Task 4: A B4MSA Ensemble based on Genetic Programming for Twitter Sentiment Analysis

Sabino Miranda-Jiménez, Mario Graff, Eric Sadit Tellez, Daniela Moctezuma

Abstract
This paper describes the system used in SemEval-2017 Task 4 (Subtask A): Message Polarity Classification for both English and Arabic languages. Our proposed system is an ensemble of two layers, the first one uses our generic framework for multilingual polarity classification (B4MSA) and the second layer combines all the decision function values predicted by B4MSA systems using a non-linear function evolved using a Genetic Programming system, EvoDAG. With this approach, the best performances reached by our system were macro-recall 0.68 (English) and 0.477 (Arabic) which set us in sixth and fourth positions in the results table, respectively.
Anthology ID:
S17-2130
Volume:
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
Month:
August
Year:
2017
Address:
Vancouver, Canada
Editors:
Steven Bethard, Marine Carpuat, Marianna Apidianaki, Saif M. Mohammad, Daniel Cer, David Jurgens
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
771–776
Language:
URL:
https://aclanthology.org/S17-2130
DOI:
10.18653/v1/S17-2130
Bibkey:
Cite (ACL):
Sabino Miranda-Jiménez, Mario Graff, Eric Sadit Tellez, and Daniela Moctezuma. 2017. INGEOTEC at SemEval 2017 Task 4: A B4MSA Ensemble based on Genetic Programming for Twitter Sentiment Analysis. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 771–776, Vancouver, Canada. Association for Computational Linguistics.
Cite (Informal):
INGEOTEC at SemEval 2017 Task 4: A B4MSA Ensemble based on Genetic Programming for Twitter Sentiment Analysis (Miranda-Jiménez et al., SemEval 2017)
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https://preview.aclanthology.org/fix-dup-bibkey/S17-2130.pdf
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