Automatically generate sentiment lexicon for the Persian stock market

With the significant growth of social media, individuals and organizations are increasingly using public opinion in these media to make their own decisions. The purpose of Sentiment Analysis is to automatically extract peoplechr('39')s emotions from these social networks. Social networks related to financial markets, including stock markets, have recently attracted the attention of many individuals and organizations. People on these social networks share their opinions and ideas about each share in the form of a post or tweet. In fact, sentiment analysis in this area is measuring peoplechr('39')s attitudes toward each share. One of the basic approaches in automatic analysis of emotions is lexicon-based methods. Most conventional lexicon is manually extracted, which is a very difficult and costly process. In this article, a new method for extracting a lexicon automatically in the field of stock social networks is proposed. A special feature of these networks is the availability of price information per share. Taking into account the price information of the share on the day of tweeting for that share, we extracted lexicon to improve the quality of opinion mining in these social networks. To evaluate the lexicon produced using the proposed method, we compared it with the Persian version of the SentiStrength lexicon, which is designed for general purpose. Experimental results show a 20% improvement in accuracy compared to the use of general lexicon.