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.
Cite
CITATION STYLE
Mansar, Y., Gatti, L., Ferradans, S., Guerini, M., & Staiano, J. (2017). Fortia-FBK at SemEval-2017 Task 5: Bullish or Bearish? Inferring Sentiment towards Brands from Financial News Headlines. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (pp. 817–822). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/S17-2138
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