Abstract
In this paper, we propose a classifier for predicting topic-specific sentiments of English Twitter messages. Our method is based on a 2-layer CNN.With a distant supervised phase we leverage a large amount of weakly-labelled training data. Our system was evaluated on the data provided by the SemEval-2017 competition in the Topic-Based Message Polarity Classification subtask, where it ranked 4th place.- Anthology ID:
- S17-2129
- 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:
- 766–770
- Language:
- URL:
- https://aclanthology.org/S17-2129
- DOI:
- 10.18653/v1/S17-2129
- Cite (ACL):
- Simon Müller, Tobias Huonder, Jan Deriu, and Mark Cieliebak. 2017. TopicThunder at SemEval-2017 Task 4: Sentiment Classification Using a Convolutional Neural Network with Distant Supervision. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 766–770, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- TopicThunder at SemEval-2017 Task 4: Sentiment Classification Using a Convolutional Neural Network with Distant Supervision (Müller et al., SemEval 2017)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/S17-2129.pdf