Abstract
This paper describes the TWINA system, with which we participated in SemEval-2017 Task 4B (Topic Based Message Polarity Classification – Two point scale) and 4D (two-point scale Tweet quantification). We implemented ensemble based Gradient Boost Trees classification method for both the tasks. Our system could perform well for the task 4D and ranked 13th among 15 teams, for the task 4B our model ranked 23rd position.- Anthology ID:
- S17-2109
- 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:
- 659–663
- Language:
- URL:
- https://aclanthology.org/S17-2109
- DOI:
- 10.18653/v1/S17-2109
- Cite (ACL):
- Naveen Kumar Laskari and Suresh Kumar Sanampudi. 2017. TWINA at SemEval-2017 Task 4: Twitter Sentiment Analysis with Ensemble Gradient Boost Tree Classifier. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 659–663, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- TWINA at SemEval-2017 Task 4: Twitter Sentiment Analysis with Ensemble Gradient Boost Tree Classifier (Laskari & Sanampudi, SemEval 2017)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/S17-2109.pdf