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
- 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:
- 648–652
- Language:
- URL:
- https://aclanthology.org/S17-2107
- DOI:
- 10.18653/v1/S17-2107
- 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)
- PDF:
- https://preview.aclanthology.org/ml4al-ingestion/S17-2107.pdf