区域事件声誉分析的情感极性分类器

Tatsuya Ohbe, Tadachika Ozono, T. Shintani
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引用次数: 6

摘要

分析地区活动(如学校节日)的声誉或需求是很重要的。在我们的工作中,我们使用情感极性分类来协调区域事件声誉。在之前的研究中,我们提出了基于词袋模型的情感极性分类。为了克服传统模型,我们提出了几种基于深度学习模型的分类器模型。作为应用程序,我们还描述了系统支持分析区域事件声誉的概述以及使用我们的系统进行区域事件分析的示例。在本文中,我们描述了如何使用深度学习模型来提高情感极性分类的性能。我们从分类精度和训练速度两方面比较了四种模型的性能。我们发现基于卷积神经网络的三词卷积模型是四种模型中最好的模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A sentiment polarity classifier for regional event reputation analysis
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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