Financial sentiment analysis model utilizing knowledge-base and domain-specific representation

Financial sentiment analysis is a very challenging problem because the market is influenced by various factors, such as company-specific/political news, sentiment/opinions of users, and other regional financial market. Good news can drive the market to grow positively, while negative news can drag the market downwards. For this reason, it is crucial to understand the impacts of news and social media on the stock market trends. Motivated by this, this paper focuses on developing an effective and efficient company-specific financial sentiment analysis model which can detect the trends of a company's stock price. More specifically, we develop a novel neural network model that transforms pretrained general word embeddings into domain-specific embeddings. In addition, we use a knowledge-base to enrich the training vocabulary, and thus extend the domain-specific embedding space. The main challenge for natural language processing (NLP) applications is to learn the representation for the rare and unseen words. Another challenge for financial sentiment analysis models addressed in this paper is to deal with words that change their polarities depending upon the domain in which they are used. We thoroughly evaluate the performance of the proposed model on a benchmark dataset of SemEval-2017 shared task on financial sentiment analysis. The experimental results show that the proposed model delivers state-of-the-art performance when applied on Twitter and news headlines datasets, thus demonstrating its feasibility and effectiveness.