2010 

Tumasjan, Andranik, et al. "Predicting elections with twitter: What 140 characters reveal about political sentiment." Fourth international AAAI conference on weblogs and social media. 2010.  被引用次数：2594

[paper]

主要思想：首次使用Twitter平台进行政治情感分析。作者在10万twitter文章数据上进行分析发现，Twitter的确可以用来反映选举结果。通过统计每个party的twitter文章流量占比，能够得到近似选举投票结果，从而可以预测选举。

2011

Chung, Jessica Elan, and Eni Mustafaraj. "Can collective sentiment expressed on twitter predict political elections?." Twenty-Fifth AAAI Conference on Artificial Intelligence. 2011.  被引用次数：156

[paper]

主要思想：对美国麻省州选进行预测，作者发现仅仅使用上面这篇文章使用Twitter流量预测是不够，于是人工标注了一批数据，使用包含情感词汇的词典（sentiment lexicons）对文本进行分类，分成positive, negative, neutral三类，从对文本进行情感分析，得到支持、反对、和中立的类别，最终可以预测竞选结果。

Bermingham, Adam, and Alan Smeaton. "On using Twitter to monitor political sentiment and predict election results." Proceedings of the Workshop on Sentiment Analysis where AI meets Psychology (SAAIP 2011). 2011.  被引用次数：310

[paper]

主要思想：作者设计了一种综合twitter流量和情感分析的模型进行选举结果预测。作者对10年的方法进行改进，原方法没有考虑party大小的影响。然后作者使用文档中的单词作为向量，训练分类器（SVM）对文本进行情感分析。

2013

Mejova, Yelena, Padmini Srinivasan, and Bob Boynton. "Gop primary season on twitter: popular political sentiment in social media." Proceedings of the sixth ACM international conference on Web search and data mining. ACM, 2013.  被引用次数：357

[paper]

2014

Ceron, Andrea, et al. "Every tweet counts? How sentiment analysis of social media can improve our knowledge of citizens’ political preferences with an application to Italy and France." New media & society 16.2 (2014): 340-358.   被引用次数：73

[paper]

2015 

Mohammad, Saif M., et al. "Sentiment, emotion, purpose, and style in electoral tweets." Information Processing & Management 51.4 (2015): 480-499.  被引用次数：100

[paper]

主要思想：作者人工标注了emotion state数据集，设计特征，训练分类器进行emotion识别。

Chambers, Nathanael, et al. "Identifying political sentiment between nation states with social media." Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. 2015.  被引用次数：22

[paper]

2016

Guan, Ziyu , et al. "Weakly-supervised deep learning for customer review sentiment classification." International Joint Conference on Artificial Intelligence AAAI Press, 2016.

2017 

Maynard, Diana, et al. "A framework for real-time semantic social media analysis." Journal of Web Semantics 44 (2017): 75-88.  被引用次数：23

[paper]

Ebrahimi, Monireh, Amir Hossein Yazdavar, and Amit Sheth. "Challenges of sentiment analysis for dynamic events." IEEE Intelligent Systems 32.5 (2017): 70-75.   被引用次数：29

[paper]

2018

Gorodnichenko, Yuriy, Tho Pham, and Oleksandr Talavera. Social media, sentiment and public opinions: Evidence from# Brexit and# USElection. No. w24631. National Bureau of Economic Research, 2018. 被引用次数：24

[paper]

Dorle, Saurabh, and Nitin Pise. "Political Sentiment Analysis through Social Media." 2018 Second International Conference on Computing Methodologies and Communication (ICCMC). IEEE, 2018.   被引用次数：1

[paper]

Yang, Xiao, Craig Macdonald, and Iadh Ounis. "Using word embeddings in twitter election classification." Information Retrieval Journal 21.2-3 (2018): 183-207.  被引用次数：61

[paper]

主要思想：embedding+CNN进行Twitter情感分析，使用两种embeddings进行实验。

Dorle, Saurabh, and Nitin Pise. "Political Sentiment Analysis through Social Media." 2018 Second International Conference on Computing Methodologies and Communication (ICCMC). IEEE, 2018. 被引用次数：1

[paper]

2019 

Badawy, Adam, Kristina Lerman, and Emilio Ferrara. "Who Falls for Online Political Manipulation?." Companion Proceedings of The 2019 World Wide Web Conference. ACM, 2019.  被引用次数：6

[paper]