Semi-Automatic Training Set Construction for Supervised Sentiment Analysis in Polarized Contexts

Standard sentiment analysis techniques usually rely either on sets of rules based on semantic and affective information or in machine learning approaches whose quality heavily depend on the size and significance of a training set of pre-labeled text samples. In many situations, this labeling needs to be performed by hand, potentially limiting the size of the training set. In order to address this issue, in this work we propose a methodology to retrieve text samples from Twitter and automatically label them. Additionally, we also tackle the situation in which the base rates of positive and negative sentiment samples in the training and test sets are biased with respect to the system in which the classifier is intended to be applied.