Fortia-FBK at SemEval-2017 Task 5: Bullish or Bearish? Inferring Sentiment towards Brands from Financial News Headlines
Youness Mansar, Lorenzo Gatti, Sira Ferradans, Marco Guerini, Jacopo Staiano
Abstract
In this paper, we describe a methodology to infer Bullish or Bearish sentiment towards companies/brands. More specifically, our approach leverages affective lexica and word embeddings in combination with convolutional neural networks to infer the sentiment of financial news headlines towards a target company. Such architecture was used and evaluated in the context of the SemEval 2017 challenge (task 5, subtask 2), in which it obtained the best performance.- Anthology ID:
- S17-2138
- 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:
- 817–822
- Language:
- URL:
- https://aclanthology.org/S17-2138
- DOI:
- 10.18653/v1/S17-2138
- Cite (ACL):
- Youness Mansar, Lorenzo Gatti, Sira Ferradans, Marco Guerini, and Jacopo Staiano. 2017. Fortia-FBK at SemEval-2017 Task 5: Bullish or Bearish? Inferring Sentiment towards Brands from Financial News Headlines. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 817–822, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Fortia-FBK at SemEval-2017 Task 5: Bullish or Bearish? Inferring Sentiment towards Brands from Financial News Headlines (Mansar et al., SemEval 2017)
- PDF:
- https://preview.aclanthology.org/emnlp22-frontmatter/S17-2138.pdf