Competitive Self-Training technique for sentiment analysis in mass social media

This paper aims to analyze user's emotion automatically by analyzing Twitter using "data without sentiment labels", not only "data with sentiment labels", to increase accuracy of sentiment analysis through an improved Self-Training, one of Semi-supervised learning techniques. Self-Training has a weak point that a classification mistake can reinforce itself. Self-Training iteratively modifies the model based on the output of the model. Thus, if the model generates wrong output, the model can be wrongly modified. For alleviate this weak point, we propose a competitive Self-Training technique. We create three models based on the output of the model and choose the best. Three models are created by binary mixture perspectives: the threshold, the same number, and the maximum number for updates. We repeat step that creating model and choosing a best model highest to get F-measure. Finally, we can improve the performance of sentiment analysis model.