NILC-USP at SemEval-2017 Task 4: A Multi-view Ensemble for Twitter Sentiment Analysis

Edilson Anselmo Corrêa Júnior, Vanessa Queiroz Marinho, Leandro Borges dos Santos


Abstract
This paper describes our multi-view ensemble approach to SemEval-2017 Task 4 on Sentiment Analysis in Twitter, specifically, the Message Polarity Classification subtask for English (subtask A). Our system is a voting ensemble, where each base classifier is trained in a different feature space. The first space is a bag-of-words model and has a Linear SVM as base classifier. The second and third spaces are two different strategies of combining word embeddings to represent sentences and use a Linear SVM and a Logistic Regressor as base classifiers. The proposed system was ranked 18th out of 38 systems considering F1 score and 20th considering recall.
Anthology ID:
S17-2100
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:
611–615
Language:
URL:
https://aclanthology.org/S17-2100
DOI:
10.18653/v1/S17-2100
Bibkey:
Cite (ACL):
Edilson Anselmo Corrêa Júnior, Vanessa Queiroz Marinho, and Leandro Borges dos Santos. 2017. NILC-USP at SemEval-2017 Task 4: A Multi-view Ensemble for Twitter Sentiment Analysis. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 611–615, Vancouver, Canada. Association for Computational Linguistics.
Cite (Informal):
NILC-USP at SemEval-2017 Task 4: A Multi-view Ensemble for Twitter Sentiment Analysis (Corrêa Júnior et al., SemEval 2017)
Copy Citation:
PDF:
https://aclanthology.org/S17-2100.pdf
Code
 edilsonacjr/semeval2017