Japanese Twitter Texts Sentiment Analysis Method based on an Improvement of Bi-directional LSTM and CNN Models
Japanese Twitter Texts Sentiment Analysis Method based on an Improvement of Bi-directional LSTM and CNN Models
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发表时间：
2021
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通讯作者：
Zheng Chen;Iwao Fujino
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作者：
Zheng Chen;Iwao Fujino
本文提出了一种使用深度学习对日本Twitter文本的情感分类的方法。近年来，情绪分析已成为社交媒体采矿中最重要的问题之一。由于关于在Twitter上发布的政府政策和公司产品的意见和评估，因此对这些文本的情感分析有望在政策制定和营销策略计划中发挥重要作用。在这项研究中，我们证实了与常规RNN模型或CNN模型相比，CNN和BI-LSTM模型的引入提供了更高的分类精度。
This paper proposes a method for classifying the sentiment polarity of Japanese Twitter texts using deep learning. In recent years, sentiment analysis has become one of the most important issues in social media mining. Since there are many opinions and evaluations about government policies and corporate products posted on Twitter, sentiment analysis of these texts is expected to play a significant role in policy making and marketing strategy planning. In this study, we confirmed that the introduction of an CNN and Bi-LSTM models provides higher classification accuracy compared to conventional RNN models or CNN model.