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
Venue:
SemEval
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
817–822
Language:
URL:
https://aclanthology.org/S17-2138
DOI:
10.18653/v1/S17-2138
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/S17-2138.pdf
Poster:
 S17-2138.Poster.pdf