Abstract
This paper describes our approach for SemEval-2017 Task 4 - Sentiment Analysis in Twitter (SAT). Its five subtasks are divided into two categories: (1) sentiment classification, i.e., predicting topic-based tweet sentiment polarity, and (2) sentiment quantification, that is, estimating the sentiment distributions of a set of given tweets. We build a convolutional sentence classification system for the task of SAT. Official results show that the experimental results of our system are comparative.- Anthology ID:
- S17-2124
- 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:
- 737–740
- Language:
- URL:
- https://aclanthology.org/S17-2124
- DOI:
- 10.18653/v1/S17-2124
- Cite (ACL):
- Maoquan Wang, Shiyun Chen, Yufei Xie, and Lu Zhao. 2017. EICA at SemEval-2017 Task 4: A Simple Convolutional Neural Network for Topic-based Sentiment Classification. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 737–740, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- EICA at SemEval-2017 Task 4: A Simple Convolutional Neural Network for Topic-based Sentiment Classification (Wang et al., SemEval 2017)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/S17-2124.pdf