Fortia-FBK at SemEval-2017 Task 5: Bullish or Bearish? Inferring Sentiment towards Brands from Financial News Headlines
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Abstract (arXiv)
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.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Apr 2017.
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Citing papers per year: 2021: 4; 2022: 0; 2023: 1.
5 papers.
- 2023SEntFiN 1.0: Entity-Aware Sentiment Analysis for Financial Newscited by 18
- 2021Intelligent Trading Systems: A Sentiment-Aware Reinforcement Learning Approachcited by 2
- 2021Contextual Sentence Analysis for the Sentiment Prediction on Financial Datacited by 2
- 2021Towards Financial Sentiment Analysis in a South African Landscapecited by 0
- 2021Tracking Turbulence Through Financial News During COVID-19cited by 0
- 2023SEntFiN 1.0: Entity-Aware Sentiment Analysis for Financial News20 May 2023
- 2021Contextual Sentence Analysis for the Sentiment Prediction on Financial Data27 Dec 2021
- 2021Intelligent Trading Systems: A Sentiment-Aware Reinforcement Learning Approach14 Nov 2021
- 2021Tracking Turbulence Through Financial News During COVID-199 Sep 2021
- 2021Towards Financial Sentiment Analysis in a South African Landscape18 Jun 2021