A Sentiment Polarity Classifier for Regional Event Reputation Analysis

  • Tatsuya Ohbe Nagoya Institute of Technology
  • Tadachika Ozono Nagoya Institute of Technology
  • Toramatsu Shintani Nagoya Institute of Technology
Keywords: convolutional neural networks, recurrent neural networks, sentiment polarity classification, sentiment visualization


It is important to analyze reputation or demands for regional events, such as school festivals. In our previous works, we proposed sentiment polarity classification based on bag-of-words models and found that the traditional models were poor at classifying negative tweets. To improve the performance, we employed several classifier models based on deep learning models. 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. As a result, we found that the CNN-based model, three words convolutions, was best among the four models. As the application, we also described the overview of a system based on the classifiers, which supports to analyze regional event reputation. We showed a case of a regional event analysis of a school festival by using our system.


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