# Fortia-FBK at SemEval-2017 Task 5: Bullish or Bearish? Inferring Sentiment towards Brands from Financial News Headlines

arXiv: https://arxiv.org/abs/1704.00939 · HTML: https://mlsanity.com/paper/1704.00939/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Apr 2017.

## Built on by (most cited first, 5 of 5)

- [SEntFiN 1.0: Entity-Aware Sentiment Analysis for Financial News](https://mlsanity.com/paper/2305.12257/) (2023)
- [Intelligent Trading Systems: A Sentiment-Aware Reinforcement Learning Approach](https://mlsanity.com/paper/2112.02095/) (2021)
- [Contextual Sentence Analysis for the Sentiment Prediction on Financial Data](https://mlsanity.com/paper/2112.13790/) (2021)
- [Towards Financial Sentiment Analysis in a South African Landscape](https://mlsanity.com/paper/2106.10004/) (2021)
- [Tracking Turbulence Through Financial News During COVID-19](https://mlsanity.com/paper/2109.04369/) (2021)

## 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.