Automatic sentiment analysis of Twitter messages

Abstract

Twitter® is a microblogging service usually used as an instant communication platform. The capacity to provide information in real time has stimulated many companies to use this service to understand their consumers. In this direction, TV stations have adopted Twitter for shortening the distance between them and their viewers, and use such information as a feedback mechanism for their shows. The sentiment analysis task can be used as one such feedback mechanism. This task corresponds to classifying a text according to the sentiment that the writer intended to transmit. A classifier usually requires a pre-classifled data sample to determine the class of new data. Typically, the sample is pre-classified manually, making the process time consuming and reducing its real time applicability for big data. This paper proposes an automatic sentiment classifier for Twitter messages, and uses TV shows from Brazilian stations for benchmarking. The automatic sentiment analysis reduces human intervention and, thus, the complexity and cost of the whole process. To assess the performance of the proposed system tweets related to a Brazilian TV show were captured in a 24h interval and fed into the system. The proposed technique achieved an average accuracy of 90%.