HCS at SemEval-2017 Task 5: Polarity detection in business news using convolutional neural networks
Lidia Pivovarova, Llorenç Escoter, Arto Klami, Roman Yangarber
Abstract
Task 5 of SemEval-2017 involves fine-grained sentiment analysis on financial microblogs and news. Our solution for determining the sentiment score extends an earlier convolutional neural network for sentiment analysis in several ways. We explicitly encode a focus on a particular company, we apply a data augmentation scheme, and use a larger data collection to complement the small training data provided by the task organizers. The best results were achieved by training a model on an external dataset and then tuning it using the provided training dataset.- Anthology ID:
- S17-2143
- 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:
- 842–846
- Language:
- URL:
- https://aclanthology.org/S17-2143
- DOI:
- 10.18653/v1/S17-2143
- Cite (ACL):
- Lidia Pivovarova, Llorenç Escoter, Arto Klami, and Roman Yangarber. 2017. HCS at SemEval-2017 Task 5: Polarity detection in business news using convolutional neural networks. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 842–846, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- HCS at SemEval-2017 Task 5: Polarity detection in business news using convolutional neural networks (Pivovarova et al., SemEval 2017)
- PDF:
- https://preview.aclanthology.org/fix-dup-bibkey/S17-2143.pdf