A Sentiment Polarity Classifier for Regional Event Reputation Analysis
大部達也, 大囿忠親, 新谷虎松

アブストラクト

It is important to analyze the reputation or demands for a regional event, such as a school festival. In our work, we use sentiment polarity classification in order to coordinate regional event reputation. We proposed sentiment polarity classification based on bag-of-words models in the previous works. To get over the traditional models, we proposed several classifier models based on deep learning models. As the application, we also described the overview of a system supports to analyze regional event reputation and an example of regional event analysis using our system. In this paper, we described how to improve the performance of the sentiment polarity classification using deep learning models. We compared the performance of four models in terms of the classification accuracy and the training speed. We found the Convolutional Neural Networks based model, three words convolutions, was the best model among the four models.