The prediction of success is an extremely important task from an economic viewpoint. The income of some products or services are heavily affected by rumors spreading in the community, regardless of them being true or false.
Recently social media is gaining more and more importance concerning the information flow in our society. A significant portion of this information may be harvested by analyzing the text originating in these media. It is also unclear, how certain emotions influence revenue. Happiness may be overrated, sadness influence much more.
The goal of this thesis is to create an income prediction model using information from social media, such as Twitter. We generate features for films, based on characteristics of tweets written about these movies. To extract the emotional aspects of tweets, we use emoticons found in the texts to define emotion classes, and classify tweets into these classes.
An income prediction model is trained using conventional features and features derived from social media.