Learning the Sentiment of Soccer Fans from Data on Bets and Social Nets

In this paper we propose a Hidden Markov Model in order to predict the sentiment of soccer fans based on information regarding the result of matches. The model was constructed by data collected from a social network where fans of a soccer team periodically expressed feelings towards their team. We show that the choice of a HMM is justified due to the fact that the change in a fan´s sentiment is analogous to a Markovian process of change of state through time. A comparative evaluation will be performed between variations of the proposed models and also between the most accurate of them and classification algorithms. Second order HMM, considering the match results and fan´s gambling information, is the most accurate model.