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://www.aclweb.org/anthology/S17-2138
DOI:
10.18653/v1/S17-2138
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/S17-2138.pdf
Poster:
 S17-2138.Poster.pdf