Sentiment Analysis of Stock Market News with Semi-supervised Learning

In these days, there are many news on stock market on the Internet and investors have to understand them immediately to invest in a stock market. In this study we determine sentimental polarities of the stock market news using a polarity dictionary, which consists of terms and their polarities. To achieve our aim we have to construct the polarity dictionary automatically because of decrease of human efforts. In construction the dictionary we use a semi-supervised learning approach. In the semi-supervised approach at first we make a small polarity dictionary, which a word polarity is determined manually, and using many stock market news, which polarities are not known, new words are added in the polarity dictionary. In this paper we proposed an automatically dictionary construction approach and sentiment analysis of stock market news using the dictionary. To discuss our proposed method we compare polarities determined by a financial expert with polarities determined with our proposed method. Hence, we confirm that the proposed method can make an appropriate dictionary.