Codice 02/2017 Titolo Monitoring Rare Categories in Sentiment and Opinion Analysis - Expo Milano 2015 on Twitter Platform. Data 2017-01-13 Autore/i Arena, M.; Calissano, A.; Vantini, S. Link Download full text Abstract This paper proposes a new aggregated classification scheme aimed to support the implementation of text analysis methods in contexts characterised 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 details, it enables the analyst to enlarge the number of text categories whose proportions can be estimated preserving the estimation accuracy of standard aggregate supervised algorithms and reducing the working time w.r.t. to unconditionally increase the size of the random training set. The approach is applied to study the daily evolution of the web reputation of Expo Milano 2015, before, during and after the event. The data set is constituted by about 900,000 tweets in Italian and 260,000 tweets in English, posted about the event between March 2015 and December 2015. The analysis provides an interesting portray of the evolution of Expo stakeholders’ opinions over time and allow to identify the main drivers of Expo reputation. The algorithm will be implemented as a running option of the next release of R package ReadMe.