Abstract
This paper describes the system we have used for participating in Subtasks A (Message Polarity Classification) and B (Topic-Based Message Polarity Classification according to a two-point scale) of SemEval-2017 Task 4 Sentiment Analysis in Twitter. We used several features with a sentiment lexicon and NLP techniques, Maximum Entropy as a classifier for our system.- Anthology ID:
- S17-2106
- 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:
- 644–647
- Language:
- URL:
- https://aclanthology.org/S17-2106
- DOI:
- 10.18653/v1/S17-2106
- Cite (ACL):
- Enkhzol Dovdon and José Saias. 2017. ej-sa-2017 at SemEval-2017 Task 4: Experiments for Target oriented Sentiment Analysis in Twitter. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 644–647, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- ej-sa-2017 at SemEval-2017 Task 4: Experiments for Target oriented Sentiment Analysis in Twitter (Dovdon & Saias, SemEval 2017)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/S17-2106.pdf