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

## TL;DR

This paper presents a neural network-based method using affective lexica and word embeddings to accurately determine bullish or bearish sentiment in financial news headlines, achieving top results in a SemEval challenge.

## Contribution

It introduces a novel CNN architecture combining affective lexica and word embeddings for sentiment analysis in financial headlines, outperforming previous approaches.

## Key findings

- Achieved best performance in SemEval 2017 Task 5 subtask 2
- Demonstrated effectiveness of combining affective lexica with CNNs
- Validated approach on financial news sentiment classification

## 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.