Using a contextual entropy model to expand emotion words and their intensity for the sentiment classification of stock market news

TLDR: Experimental results show that the proposed method can discover more useful emotion words and their corresponding intensity, thus improving classification performance, and it outperformed the previously-proposed pointwise mutual information (PMI)-based expansion methods.

Abstract: Sentiment classification of stock market news involves identifying positive and negative news articles, and is an emerging technique for making stock trend predictions which can facilitate investor decision making. In this paper, we propose the presence and intensity of emotion words as features to classify the sentiment of stock market news articles. To identify such words and their intensity, a contextual entropy model is developed to expand a set of seed words generated from a small corpus of stock market news articles with sentiment annotation. The contextual entropy model measures the similarity between two words by comparing their contextual distributions using an entropy measure, allowing for the discovery of words similar to the seed words. Experimental results show that the proposed method can discover more useful emotion words and their corresponding intensity, thus improving classification performance. Performance was further improved by the incorporation of intensity into the classification, and the proposed method outperformed the previously-proposed pointwise mutual information (PMI)-based expansion methods.