International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Filtering Political Sentiment on Social Media

Author : Indrajeet Garde ¹ Shreyas Lolage ² Shekhar Sabale ³ Ajay Patil ⁴ Preeti Joshi ⁵

Date of Publication :12th August 2021

Abstract: Social Media is an incredible source of appraisals, political or otherwise, therefore the domain of sentiment analysis has seen an increase in interest over the span of the last couple of years. And given the current rise of populist leaders all around the world, gauging the publicâ€™s response to any political institution or candidate is vital. Sentiment analysis mines opinions at word, sentence, and document levels, and offers input polarities and characteristics of articles. We propose a system that filters out political data from the social network Twitter, using typical Machine Learning algorithms, and then performs sentiment analysis.Thus, we shall better understand the general publicâ€™s opinion on certain political topics.

Reference :

1. Ying Fang, Hai Tan, Jun Zhang, â€•Multi-Strategy Sentiment Analysis of Consumer Reviews Based on Semantic Fuzzinessâ€–, IEEE Access-2020.
2. Shihab Elbagir and Jing Yang, â€•Twitter Sentiment Analysis based on ordinal Regressionâ€–, IEEE Access2020.
3. Huyen Trang Phan, Van Cuong Tran, Ngoc Thanh Nguyen, and Dosam Hwang, â€•Improving the Performance of Sentiment Analysis of Tweets Containing Fuzzy Sentiment Using the Feature Ensemble Modelâ€–, IEEE Access-2020.
4. Farkhund Iqbal, Jahanzeb Maqbool, Benjamin C. M. Fung, Rabia Batool, Asad Masood Khattak, Saiqa Aleem, and Patrick C. K. Hung, â€•A Hybrid Framework for Sentiment Analysis using Genetic Algorithm based Feature Reductionâ€–, IEEE Access2020
5. Rosy Indah Permatasari, M. Ali Fauzi, Putra Pandu Adikara, â€•Twitter Sentiment Analysis of Movie Reviews using Naïve Bayesâ€–, IEEE Access-2020.
6. Saurabh Dorle, Dr.Nitin Pise, â€•Political Sentiment Analysis through Social Mediaâ€–, Proceedings of the Second International Conference on Computing Methodologies and Communication
7. M. Govindarajan, â€•Analysis of bagged ensemble classifiers for blogger dataâ€–, IEEE Access-2020. [8] Xian Fan, Xiaoge Li, Feihong Du, Xin Li, Mian Wei, â€•Apply word vectors for sentiment analysis of APP reviewsâ€–, ICSAI 2016.
8. Shihab Elbagir and Jing Yang, â€•Twitter Sentiment Analysis based on ordinal Regressionâ€–, IEEE Access2020.
9. Huyen Trang Phan, Van Cuong Tran, Ngoc Thanh Nguyen, and Dosam Hwang, â€•Improving the Performance of Sentiment Analysis of Tweets Containing Fuzzy Sentiment Using the Feature Ensemble Modelâ€–, IEEE Access-2020.
10. Farkhund Iqbal, Jahanzeb Maqbool, Benjamin C. M. Fung, Rabia Batool, Asad Masood Khattak, Saiqa Aleem, and Patrick C. K. Hung, â€•A Hybrid Framework for Sentiment Analysis using Genetic Algorithm based Feature Reductionâ€–, IEEE Access2020
11. Robert Hillmann and Matthias Trier. 2012. Dissemination patterns and associated network effects of sentiments in social networks. In Proceedings of the IEEE International Conference on Advances in Social Networks Analysis and Mining.
12. Stefan Stieglitz and Linh Dang-Xuan. 2013. Emotions and information diffusion in social media—Sentiment of microblogs and sharing behavior. J. Manag. Inf. Syst. 29, 4 (2013), 217–248
13. Jie Tang and A. C. M. Fong. 2013. Sentiment diffusion in large scale social networks. In Proceedings of the IEEE International.
14. Maria Karakiza. 2015. The impact of social media in the public sector. Procedia Soc. Behav. Sci. 175(2 2015). DOI: https://doi.org/10.1016/j.sbspro.2015.01.1214
15. Saifuddin Ahmed, Kokil Jaidka, and Jaeho Cho. 2017. Tweeting India’s Nirbhaya protest: A study of emotional dynamics in an online social movement. Soc. Mov. Stud. 16, 4 (2017), 447–465.
16. Yung-Ming Li and Ya-Lin Shiu. 2012. A diffusion mechanism for social advertising over microblogs. December Supp. Syst. 54, 1(2012), 9–22.
17. Hung T. Nguyen, Preetam Ghosh, Michael L. Mayo, and Thang N. Dinh. 2017. Social influence spectrum at scale: Near optimal solutions for multiple budgets at once. ACM Trans. Inf. Syst. 36, 2 (2017), 14.
18. Josemar A. Caetano, Hélder S. Lima, Mateus F. Santos and Humberto T. Marques-Neto, â€•Using sentiment analysis to define twitter political users’ classes and their homophily during the 2016 American presidential electionâ€–, SpringerOpen, Journal of Internet Services and Applications 2018. DOI: https://doi.org/10.1186/s13174-018-0089-0
19. Debashis Naskar, Sanasam Ranbir Singh, Durgesh Kumar, Sukumar Nandi, and Eva Onaindia de la Rivaherrera. 2020. Emotion Dynamics of Public Opinions on Twitter. ACM Trans. Inf. Syst. 38, 2, Article 18 (March 2020)