Monitoring rare categories in sentiment and opinion analysis: a Milan mega event on Twitter platform

This paper proposes a new aggregated classification scheme aimed to support the implementation of semantic text analysis methods in contexts characterized by the presence of rare text categories. The proposed approach starts from the aggregate supervised text classifier developed by Hopkins and King and moves forward, relying on rare event sampling methods. In detail, it enables the analyst to enlarge the number of estimated sentiment categories, both preserving the estimation accuracy and reducing the working time to unconditionally increase the size of the training set. The approach is applied to study the daily evolution of the web reputation of one of the last mega-event taking place in Europe: Expo Milano. The corpus consists of more than one million tweets in both Italian and English, discussing about the event. The analysis provides an interesting portrayal of the evolution of the Expo stakeholders' opinions over time and allows the identification of the main drivers of the Expo reputation. The algorithm will be implemented as a running option in the next release of the R package ReadMe.