Abstract
Sentiment analysis is the task of automatic analysis of opinions and emotions of users towards an entity or some aspect of that entity. Political Sentiment Analysis of social media helps the political strategists to scrutinize the performance of a party or candidate and improvise their weaknesses far before the actual elections. During the time of elections, the social networks get flooded with blogs, chats, debates and discussions about the prospects of political parties and politicians. The amount of data generated is much large to study, analyze and draw inferences using the latest techniques. Twitter is one of the most popular social media platforms enables us to perform domain-specific data preparation. In this work, we chose to identify the inclination of political opinions present in Tweets by modelling it as a text classification problem using classical machine learning. The tweets related to the Delhi Elections in 2020 are extracted and employed for the task. Among the several algorithms, we observe that Support Vector Machines portrays the best performance.
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References
M. Ringsquandl, D. Petković, Analyzing political sentiment on twitter, in 2013 AAAI Spring Symposium vol. SS-13-01 (2013)
D. Najar, S. Mesfar, Opinion mining and sentiment analysis for Arabic on-line texts: application on the political domain. Int. J. Speech Technol. 20(1), 1–11 (2017). https://doi.org/10.1007/s10772-017-9422-4
M.Z. Ansari, M.B. Aziz, M.O. Siddiqui, Analysis of political sentiment orientations on twitter. Procedia Comput. Sci. 167(2020), 1821–1828 (2019)
B. Pang, L. Lee, Opinion mining and sentiment analysis. Found. Trends Inf. Retrieval. 2(1–2), 1–135 (2008)
G. Xu, L. Li, Social media mining and social network analysis: emerging research and information, in Science reference. IGI Global, Hershey (2013). https://doi.org/10.4018/978-1-4666-2806-9
M.Z. Ansari, S. Khan, T. Amani, A. Hamid, S. Rizvi, Analysis of part of speech tags in language identification of code-mixed text, in Advances in Computing and Intelligent Systems. Algorithms for Intelligent Systems. (Springer, Singapore, 2020)
J. Bollen, H. Mao, X. Zeng, Twitter mood predicts the stock market. J. Comput. Sci. (2011). https://doi.org/10.1016/j.jocs.2010.12.007
M. Choy, L.F.M. Cheong, N.L. Ma, P.S. Koo, A sentiment analysis of Singapore presidential election 2011 using twitter data with census correction, in Research Collection School of Information Systems (2011)
K.M. Wagner, J. Gainous, Digital uprising: the internet revolution in the middle east. In. J. Inf Technol. Politics. (2013). https://doi.org/10.1080/19331681.2013.778802
R. Prati, E. Said-Hung, Predicting the ideological orientation during the Spanish 24 M elections in Twitter using machine learning, in AI & Society 2017 (2017). https://doi.org/10.1007/s00146-017-0761-0
A. Gruzd, J. Roy, Investigating political polarization on twitter: a canadian perspective. Policy Internet 6(1), 28–45 (2014)
G. Elmer, Live research: Twittering an election debate. New Media Soc. (2012). https://doi.org/10.1177/1461444812457328
A. Tumasjan, T.O. Sprenger, P.G. Sandner, I.M. Welpe, Predicting elections with twitter: what 140 characters reveal about political sentiment, in Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media (2010)
E. Colleoni, A. Rozza, A. Arvidsson, Echo chamber or public sphere? Predicting political orientation and measuring political homophily in Twitter using big data. J. Commun. https://doi.org/10.1111/jcom.12084 (2014)
B. O’ Connor, R. Balasubramanyan, B.R. Routledge, N.A. Smith, From tweets to poll: linking the text sentiment to public opinion time series, in Proceedings of the ICWSM Conference (2010)
H. Wang, D. Can, A. Kazemzadeh, F. Bar, S. Narayanan, A system for real-time twitter sentiment analysis of 2012 U.S. presidential election cycle, in Proceedings of the ACL 2012 System Demonstrations, pp 115–120 (2012)
M. Conover, B. Gonc alves, J. Ratkiewicz, A. Flammini, F. Menczer, Predicting the political alignment of Twitter users, in Proceedings of SocialCom/PASSAT conference, pp. 192–199 (2011)
B. Chambers, Learning for micro blogs with distant supervision: political forecasting with Twitter, in Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics (2012)
P. Dandannavar, A. Jain, Application of machine learning techniques to sentiment analysis, in IEEE 2nd International Conference on Applied and Theoretical Computing and Commuication Technology ICATCCT (2017)
V. Sahayak, A. Pathan, V. Shete, Sentiment analysis on twitter data. Int. J. Innovative Res. Adv. Eng. 2, 178–183 (2015)
K. Koc-Michalska, R. Gibson, T. Vedel, Online campaigning in France, 2007–2012: political actors and citizens in the aftermath of the Web.2.0 evolution. J. Inf. Technol. Politics (2014). https://doi.org/10.1080/19331681.2014.903217
C.F. Fominaya, Social movements and globalization: how protests, occupations ad are changing the World (Palgrave Macmillan, New York, 2014)
T. Wilson, P. Hoffmann, J. Wiebe, Recognizing contextual polarity in phrase-level sentiment analysis, in Proceedings of the 2005 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2005)
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Ansari, M.Z., Siddiqui, A.F., Anas, M. (2021). Inferring Political Preferences from Twitter. In: Tavares, J.M.R.S., Chakrabarti, S., Bhattacharya, A., Ghatak, S. (eds) Emerging Technologies in Data Mining and Information Security. Lecture Notes in Networks and Systems, vol 164. Springer, Singapore. https://doi.org/10.1007/978-981-15-9774-9_54
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