Abstract
The paper describes the participation of the team “TwiSE” in the SemEval-2017 challenge. Specifically, I participated at Task 4 entitled “Sentiment Analysis in Twitter” for which I implemented systems for five-point tweet classification (Subtask C) and five-point tweet quantification (Subtask E) for English tweets. In the feature extraction steps the systems rely on the vector space model, morpho-syntactic analysis of the tweets and several sentiment lexicons. The classification step of Subtask C uses a Logistic Regression trained with the one-versus-rest approach. Another instance of Logistic Regression combined with the classify-and-count approach is trained for the quantification task of Subtask E. In the official leaderboard the system is ranked 5/15 in Subtask C and 2/12 in Subtask E.- Anthology ID:
- S17-2127
- 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:
- 755–759
- Language:
- URL:
- https://aclanthology.org/S17-2127
- DOI:
- 10.18653/v1/S17-2127
- Cite (ACL):
- Georgios Balikas. 2017. TwiSe at SemEval-2017 Task 4: Five-point Twitter Sentiment Classification and Quantification. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 755–759, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- TwiSe at SemEval-2017 Task 4: Five-point Twitter Sentiment Classification and Quantification (Balikas, SemEval 2017)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/S17-2127.pdf