Sentiment analysis on stock social media for stock price movement prediction

The opinions of other people are an essential piece of information for making informed decisions. With the increase in using the Internet, today the web becomes an excellent source of user's viewpoints in different domains. However, in one hand, the growing volume of opinionated text and on the other hand, complexity caused by contrast in user opinion, makes it almost impossible to read all of these reviews and make an informed decision. These requirements have encouraged a new line of research on mining user reviews, which is called opinion mining. User's viewpoints could change during the time, and this is an important issue for companies. One of the most challenging problems of opinion mining is model-based opinion mining, which aim to model the generation of words by modeling their probabilities. In this paper, we address the problem of model-based opinion mining by introducing a part-of-speech graphical model to extract user's opinions and test it in two different datasets in English and Persian where the Persian dataset is gathered in this paper from Iranian stock market social network. In the prediction of the stock market by this model, we achieved an accuracy better than methods that are using explicit sentiment labels for comments.