Please wait a minute...
Data Analysis and Knowledge Discovery  2021, Vol. 5 Issue (3): 121-131    DOI: 10.11925/infotech.2096-3467.2020.0743
Current Issue | Archive | Adv Search |
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
Download: PDF (915 KB)   HTML ( 9
Export: BibTeX | EndNote (RIS)      

[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.

Key wordsTwitter      Dynamic Sentiment Analysis      Politician Group      Deep Learning      BERT     
Received: 29 July 2020      Published: 24 November 2020
ZTFLH:  TP391  
Fund:State Key Laboratory of the Science and Technology Foundation(6142110180405)

Cite this article:

Chang Chengyang,Wang Xiaodong,Zhang Shenglei. Polarity Analysis of Dynamic Political Sentiments from Tweets with Deep Learning Method. Data Analysis and Knowledge Discovery, 2021, 5(3): 121-131.

URL:     OR

Political Sentiment Analysis Framework
分类 政治家姓名
John Bolton, Donald Trump, Mike Pence, RoBert O’brien, Mike Pompeo, Steven Mnuchin
Congressmen Frank Palone, Congressman Eric Swawell, Senator Richard Blumenthal
Joe Biden, Congressmen Adam Schiff, Senator Bernie Sanders, Speakers Nancy Pelosi
Governor Gretchen Whitmer, Senator Kamala Harris, Lawrence H. Summers, Governor Andrew Cuomo, Sally Yates, Senator Maria Cantwell, Senator Edward Markey, Senator Elizabeth Warren
Politicians of Each Category Selected in the Dataset
推文内容 标签
Enforcers must stop scammers and bottom feeders from exploiting COVID-19 and endangering health. False pitches and sky-high price hikes should be halted and prosecuted. 1
Enjoyed talking davidgura at Select USA summit. Tax reform trade and regulation rollback are critical to serve hardworking Americans. 0
President Trump may be a slick salesman who fooled many people in this country, but you didn’t fool me and you didn’t fool New Yorkers. 3
With respect, Mr. President, not sure we can rely on Mr. Manafort’s lawyer to tell us whether there was collusion, as unbiased as he may be. 2
Twitter Example in Dataset After Cleaning
数据集 类别1 类别2 类别3 类别4 总计
训练集 9 300 6 430 7 427 14 905 38 062
验证集 1 861 1 286 1 486 2 981 7 614
测试集 1 862 1 286 1 486 2 982 7 616
总计 13 023 9 002 10 399 20 868 53 292
Details of Each Classification of the Data Set
模型 验证集准确率 模型Loss F1值 训练速度(epoch=3) 模型大小(循环最后一个参数文件)
CNN 63.79% 0.827 1 62.35% 12分19秒 54.3MB
C-LSTM 67.56% 0.664 5 66.87% 14分15秒 69.3MB
Bi-LSTM 72.59% 0.736 5 71.83% 13分42秒 55.1MB
BERT 80.66% 0.628 2 79.34% 52分23秒 1.22GB
Experimental Results of Different Classifiers
身份 人数 正确判别 错误判别
参议员 10 7 3
州长 10 8 2
总计 20 15 5
Discrimination Results for 20 Senators and Governors
姓名 时间段 职务 类别1概率 类别2概率 类别3概率 类别4概率
Phil Murphy 2020-02-05-2020-05-24 Governor of New Jersey 0.098 0.454 0.019 0.428
Richard Mike De Wine 2019-12-20-2020-05-24 Governor of Ohio 0.357 0.034 0.019 0.590
John Carney 2016-06-07-2015-05-24 Governor of Delaware 0.179 0.214 0.050 0.557
Eric Brakey 2018-08-26-2020-05-23 Senator in Maine 0.347 0.131 0.220 0.302
Laura Kelly 2019-01-14-2020-05-22 Governor of Kansas 0.434 0.016 0.090 0.460
The Results of Judging Tweets from 5 US Politicians
姓名 党派 职务 主要的政治观点 对特朗普
Phil Murphy 民主党 Governor of New Jersey 对亚裔、有色人种持同情态度,在抗疫问题上赞成特朗普,感谢特朗普政府给予的大量支援 中性 属于温和的反对派,在尽管和特朗普有党派不同之分,但是在大部分情况下还是跟随特朗普政府,支持特朗普政府的决定。分类器也将此人归类为此类
Richard Mike De Wine 共和党 Governor of Ohio 不同于共和党支持控枪,但是反对同性婚姻、反对堕胎,同时积极防控新冠疫情 支持 与普通的共和党极右翼人员有很大不同,虽然支持特朗普政府,但是在很多立场上是相背离的。分类器展现的也是这个判断
John Carney 民主党 Governor of Delaware 支持保护有色人种权益,强调种族平等、支持控枪、积极组织抗击疫情 强烈反对 自特朗普政府上台之后,此人就对其持反对意见。分类器的结果也是如此
Eric Brakey 共和党 Senator in Maine 传统的共和党参议员,有强烈的反中倾向 支持 反中倾向极强,对特朗普政府的主要政治观点都是非常赞同的。分类器结果与实际结果有所偏差,不过Lable=0概率达到0.347,已经非常高
Laura Kelly 民主党 Governor of Kansas 传统的民主党州长,支持全民医保,积极抗击新冠疫情,但是其所在州是传统共和党票仓,得到了大量联邦的医疗资源 反对 民主党人士,在抗击疫情问题上强烈抨击特朗普政府。但是判别器却给出相反的结论,原因可能因为其推文发布有大量感谢特朗普政府支援物资的内容,并没有真实反映其政治取向
Analysis of Political Sentiment Polarity of 5 American Politicians
推文发表时间 推文内容
['2020-05-21'] By withdrawing from the Open Skies Treaty, Pres. Trump is barreling down a path that makes us less secure,... I urge the President to reverse this reckless decision.
['2020-05-14'] ...With limited supplies, I'm calling on the Trump administration to be transparent with the American people about how this drug will be distributed.
Some Senator Shaheen’ Twitter
Dynamic Political Sentiment Analysis Results with March 20, 2020 as Time Node of Special Event
[1] Twitter , Inc . Financial Information 2020 Annual Report[EB/OL].
[2] Bermingham A, Smeaton A F. On Using Twitter to Monitor Political Sentiment and Predict Election Results[C]// Proceedings of the Workshop on Sentiment Analysis Where AI Meets Psychology (SAAIP 2011). 2011: 2-10.
[3] 刘志明, 刘鲁. 基于机器学习的中文微博情感分类实证研究[J]. 计算机工程与应用, 2012,48(1):1-4.
[3] ( Liu Zhiming, Liu Lu. Empirical Study of Sentiment Classification for Chinese Microblog Based on Machine Learning[J]. Computer Engineering and Applications, 2012,48(1):1-4.)
[4] Yang Z C, Yang D Y, Dyer C, et al. Hierarchical Attention Networks for Document Classification[C]// Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2016: 1480-1489.
[5] Kim Y. Convolutional Neural Networks for Sentence Classification[C]// Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). 2014: 1746-1751.
[6] Kalchbrenner N, Grefenstette E, Blunsom P. A Convolutional Neural Network for Modelling Sentences[C]// Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2014: 655-665.
[7] Yin W P, Schütze H. Multichannel Variable-Size Convolution for Sentence Classification[OL]. arXiv Preprint, arXiv: 1603. 04513.
[8] Tai K S, Socher R, Manning C D. Improved Semantic Representations from Tree-Structured Long Short-Term Memory Networks[OL]. arXiv Preprint, arXiv: 1503. 00075.
[9] Ding Z X, Xia R, Yu J F, et al. Densely Connected Bidirectional LSTM with Applications to Sentence Classification[C]// Proceedings of CCF International Conference on Natural Language Processing and Chinese Computing. Springer, Cham, 2018: 278-287.
[10] Zhou C T, Sun C L, Liu Z Y, et al. A C-LSTM Neural Network for Text Classification[OL]. arXiv Preprint, arXiv: 1511. 08630.
[11] Lai S W, Xu L H, Liu K, et al. Recurrent Convolutional Neural Networks for Text Classification[C]// Proceedings of the 29th AAAI Conference on Artificial Intelligence. 2015: 2267-2273.
[12] Socher R, Perelygin A, Wu J, et al. Recursive Deep Models for Semantic Compositionality over a Sentiment Treebank[C]// Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing. 2013: 1631-1642.
[13] Li X, Roth D. Learning Question Classifiers[C]// Proceedings of the 19th International Conference on Computational Linguistics. 2002: 1-7.
[14] 王芝辉, 王晓东. 基于神经网络的文本分类方法研究[J]. 计算机工程, 2020,46(3):11-17.
[14] ( Wang Zhihui, Wang Xiaodong. Research on Text Classification Method Based on Neural Network[J]. Computer Engineering, 2020,46(3):11-17.)
[15] Reddy D M, Reddy N V S. Twitter Sentiment Analysis Using Distributed Word and Sentence Representation[OL]. arXiv Preprint, arXiv: 1904. 12580.
[16] Dunnmon J, Ganguli S, Hau D, et al. Predicting State-Level Agricultural Sentiment with Tweets from Farming Communities[OL]. arXiv Preprint,arXiv: 1902. 07087.
[17] Dai X F, Bikdash M, Meyer B. From Social Media to Public Health Surveillance: Word Embedding Based Clustering Method for Twitter Classification[C]// Proceedings of SoutheastCon 2017. DOI: 10.1109/SECON.2017.7925400.
[18] Gencoglu O. Deep Representation Learning for Clustering of Health Tweets[OL]. arXiv Preprint,arXiv: 1901. 00439.
[19] Biseda B, Mo K. Enhancing Pharmacovigilance with Drug Reviews and Social Media[OL]. arXiv Preprint,arXiv: 2004. 08731.
[20] Devlin J, Chang M W, Lee K, et al. BERT: Pre-Training of Deep Bidrectional Transformers for Language Understanding[C]// Proceedings of NAACL-HLT 2019. 2019:4171-4186.
[21] Lee J, Yoon W, Kim S, et al. BioBERT: a Pre-Trained Biomedical Language Representation Model for Biomedical Text Mining[J]. Bioinformatics, 2019,36(4). DOI: 10.1093/bioinformatics/btz682.
pmid: 31584610
[22] Nikfarjam A, Sarker S, O’Connor K, et al. Pharmacovigilance from Social Media: Mining Adverse Drug Reaction Mentions Using Sequence Labeling with Word Embedding Cluster Features[J]. Journal of the American Medical Informatics Association, 2015,22(3):671-681.
doi: 10.1093/jamia/ocu041 pmid: 25755127
[23] Müller M, Salathé M, Kummervold P E. COVID-Twitter-BERT: A Natural Language Processing Model to Analyse COVID-19 Content on Twitter[OL]. arXiv Preprint,arXiv: 2005. 07503.
[24] Yin H, Yang S Q, Li J X. Detecting Topic and Sentiment Dynamics Due to COVID-19 Pandemic Using Social Media[OL]. arXiv Preprint,arXiv: 2007. 02304.
[25] Hutto C J, Gilbert E. VADER: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text[C]// Proceedings of the 8th International AAAI Conference on Weblogs and Social Media. 2014.
[26] 李慧, 胡云凤. 基于动态情感主题模型的在线评论分析[J]. 数据分析与知识发现, 2017,1(9):74-82.
[26] ( Li Hui, Hu Yunfeng. Analyzing Online Reviews with Dynamic Sentiment Topic Model[J]. Data Analysis and Knowledge Discovery, 2017,1(9):74-82.)
[27] 熊蜀峰, 姬东鸿. 面向产品评论分析的短文本情感主题模型[J]. 自动化学报, 2016,42(8):1227-1237.
[27] ( Xiong Shufeng, Ji Donghong. A Short Text Sentiment-Topic Model for Product Review Analysis[J]. Acta Automatica Sinica, 2016,42(8):1227-1237.)
[28] Gupta V, Aggarwal A, Chakraborty T. Detecting and Characterizing Extremist Reviewer Groups in Online Product Reviews[J]. IEEE Transactions on Computational Social Systems, 2020,7(3):741-750.
[29] Minaee S, Azimi E, Abdolrashidi A A. Deep-Sentiment: Sentiment Analysis Using Ensemble of CNN and Bi-LSTM Models[OL]. arXiv Preprint, arXiv: 1904. 04206.
[1] Cheng Bin,Shi Shuicai,Du Yuncheng,Xiao Shibin. Keyword Extraction for Journals Based on Part-of-Speech and BiLSTM-CRF Combined Model[J]. 数据分析与知识发现, 2021, 5(3): 101-108.
[2] Feng Yong,Liu Yang,Xu Hongyan,Wang Rongbing,Zhang Yonggang. Recommendation Model Incorporating Neighbor Reviews for GRU Products[J]. 数据分析与知识发现, 2021, 5(3): 78-87.
[3] Hu Haotian,Ji Jinfeng,Wang Dongbo,Deng Sanhong. An Integrated Platform for Food Safety Incident Entities Based on Deep Learning[J]. 数据分析与知识发现, 2021, 5(3): 12-24.
[4] Zhang Qi,Jiang Chuan,Ji Youshu,Feng Minxuan,Li Bin,Xu Chao,Liu Liu. Unified Model for Word Segmentation and POS Tagging of Multi-Domain Pre-Qin Literature[J]. 数据分析与知识发现, 2021, 5(3): 2-11.
[5] Wang Qian,Wang Dongbo,Li Bin,Xu Chao. Deep Learning Based Automatic Sentence Segmentation and Punctuation Model for Massive Classical Chinese Literature[J]. 数据分析与知识发现, 2021, 5(3): 25-34.
[6] Lv Xueqiang,Luo Yixiong,Li Jiaquan,You Xindong. Review of Studies on Detecting Chinese Patent Infringements[J]. 数据分析与知识发现, 2021, 5(3): 60-68.
[7] Li Danyang, Gan Mingxin. Music Recommendation Method Based on Multi-Source Information Fusion[J]. 数据分析与知识发现, 2021, 5(2): 94-105.
[8] Liu Huan,Zhang Zhixiong,Wang Yufei. A Review on Main Optimization Methods of BERT[J]. 数据分析与知识发现, 2021, 5(1): 3-15.
[9] Huang Lu,Zhou Enguo,Li Daifeng. Text Representation Learning Model Based on Attention Mechanism with Task-specific Information[J]. 数据分析与知识发现, 2020, 4(9): 111-122.
[10] Yu Chuanming, Wang Manyi, Lin Hongjun, Zhu Xingyu, Huang Tingting, An Lu. A Comparative Study of Word Representation Models Based on Deep Learning[J]. 数据分析与知识发现, 2020, 4(8): 28-40.
[11] Zhao Yang, Zhang Zhixiong, Liu Huan, Ding Liangping. Classification of Chinese Medical Literature with BERT Model[J]. 数据分析与知识发现, 2020, 4(8): 41-49.
[12] Xu Chenfei, Ye Haiying, Bao Ping. Automatic Recognition of Produce Entities from Local Chronicles with Deep Learning[J]. 数据分析与知识发现, 2020, 4(8): 86-97.
[13] Wang Xinyun,Wang Hao,Deng Sanhong,Zhang Baolong. Classification of Academic Papers for Periodical Selection[J]. 数据分析与知识发现, 2020, 4(7): 96-109.
[14] Jiao Qihang,Le Xiaoqiu. Generating Sentences of Contrast Relationship[J]. 数据分析与知识发现, 2020, 4(6): 43-50.
[15] Wang Mo,Cui Yunpeng,Chen Li,Li Huan. A Deep Learning-based Method of Argumentative Zoning for Research Articles[J]. 数据分析与知识发现, 2020, 4(6): 60-68.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938