Fortia-FBK at SemEval-2017 Task 5: Bullish or Bearish? Inferring Sentiment towards Brands from Financial News Headlines
This work addresses sentiment analysis for financial applications, but it is incremental as it combines existing methods (affective lexica, word embeddings, and CNNs) for a specific task.
The paper tackled the problem of inferring bullish or bearish sentiment towards companies from financial news headlines, achieving the best performance in the SemEval 2017 challenge (task 5, subtask 2).
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.