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A sentiment polarity classifier for regional event reputation analysis

Published: 23 August 2017 Publication History

Abstract

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.

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  1. A sentiment polarity classifier for regional event reputation analysis

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      cover image ACM Conferences
      WI '17: Proceedings of the International Conference on Web Intelligence
      August 2017
      1284 pages
      ISBN:9781450349512
      DOI:10.1145/3106426
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      Published: 23 August 2017

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      Author Tags

      1. convolutional neural networks
      2. recurrent neural networks
      3. sentiment polarity classification
      4. sentiment visualization

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      • (2021)Sustainable Business and Collaboration Driven by Big Data Analytics Amidst the Emergence of the Remote Work CultureRemote Work and Sustainable Changes for the Future of Global Business10.4018/978-1-7998-7513-0.ch002(15-32)Online publication date: 2021
      • (2020)Big Data ProcessingApplications and Approaches to Object-Oriented Software Design10.4018/978-1-7998-2142-7.ch005(111-132)Online publication date: 2020

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