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| Polarity Analysis of Dynamic Political Sentiments from Tweets with Deep Learning Method |
Chang Chengyang1,Wang Xiaodong1( ),Zhang Shenglei2 |
1College of Computer Science, National University of Defence Technology, Changsha 410073, China
2Staff of the Space Systems Department, Strategic Support Force, Beijing 100094, China |
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Abstract [Objective] This paper studies the polarity of dynamic political sentiments from U.S. politicians’ tweets, aiming to analyze the future directions of U.S. politics and the China-US relations. [Methods] First, we proposed a framework combining multiple deep learning models. Then, we constructed tweet dataset from politicians and obtained a multi-classifier for sentiment polarity. Third, we added the tweets’ time characteristics to find the dynamic political sentiment polarity. [Results] We examined our framework with tweets from 20 U.S. governors and senators. Its accuracy reached 80.66%, which was 8.07% higher than that of the traditional artificial neural network method. The success rate of sentiment polarity analysis was 75%. [Limitations] The analysis of dynamic political sentiment polarity depends on the regular update and iteration of the data set, otherwise the accuracy and effectiveness of the model will decrease with the change of time; political sentiment polarity is affected by many factors, and the emotional content of politicians’ tweets may be different from the real political tendency they represent, which will lead to a certain degree of misjudgment of the model. [Conclusions] The proposed method helps intelligence analysts effectively obtain polarity of dynamic political sentiments from massive Twitter text data.
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Received: 29 July 2020
Published: 24 November 2020
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| Fund:State Key Laboratory of the Science and Technology Foundation(6142110180405) |
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Corresponding Authors:
Wang Xiaodong
E-mail: xdwang@nudt.edu.cn
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