TSentiment: On gamifying Twitter sentiment analysis

Social media platforms contain interesting information that can be used to directly measure people' feelings and, thanks to the use of communication technologies, also to geographically locate these feelings. Unfortunately, the understanding is not as easy as one may think. Indeed, the large volume of data makes the manual approach impractical and the diversity of language combined with the brevity of the texts makes the automatic approach quite complicated. In this paper, we consider the gamification approach to sentimentally classify tweets and we propose TSentiment, a game with a purpose that uses human beings to classify the polarity of tweets (e.g., positive, negative, neutral) and their sentiment (e.g., joy, surprise, sadness, etc.). We created a dataset of more than 65,000 tweets, we developed a Web-based game and we asked students to play the game. Obtained results showed that the game approach was well accepted and thus it can be useful in scenarios where the identification of people' feelings may bring benefits to decision making processes.