Abstract
Sentiment analysis is a vital research topic in the field of Computer Science. With the accelerated development of information technology and social networks, a massive amount of data related to comment texts has been generated on web applications or social media platforms like Twitter. Due to this, people have actively started proliferating general information and the information related to political opinions, which becomes an important reason for analyzing public reactions. Most researchers have used social media specifics or contents to analyze and predict public opinion concerning political events. This research proposes an analytical study using Israeli political Twitter data to interpret public opinion toward the Palestinian-Israeli conflict. The attitudes of ethnic groups and opinion leaders in the form of tweets are analyzed using machine learning algorithms like support vector classifier (SVC), decision tree (DT), and Naïve Bayes (NB). Finally, a comparative analysis is done based on experimental results from different models.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Alamoodi, A. H. et al.; "Sentiment Analysis and Its Applications in Fighting COVID-19 and Infectious Diseases: A Systematic Review."; Expert Systems with Applications; 2021.
Feldman, Ronen; "Techniques and Applications for Sentiment Analysis: The Main Applications and Challenges of One of the Hottest Research Areas in Computer Science."; Communications of the ACM; 2019.
Rahmatika et al.; "The Effectiveness of Youtube as an Online Learning Media"; Journal of Education Technology; 2021.
Tafesse, Wondwesen; "YouTube Marketing: How Marketers' Video Optimization Practices Influence Video Views."; Internet Research 30.6; 2020.
Bozkurt et al.; "Cleft Lip and Palate YouTube Videos: Content Usefulness and Sentiment Analysis."; Cleft Palate-Craniofacial Journal 58.3; 2021.
Al-Sarraj et al.; "Bias Detection of Palestinian/Israeli Conflict in Western Media: A Sentiment Analysis Experimental Study."; International Conference on Promising Electronic Technologies; 2018.
Cambria, Erik; "Affective Computing and Sentiment Analysis."; IEEE; 2016.
Yadav, Ashima et al.; "Sentiment Analysis Using Deep Learning Architectures: A Review."; Artificial Intelligence Review 53.6; 2020.
Doaa Mohey et al; "A Survey on Sentiment Analysis Challenges."; Journal of King Saud University - Engineering Sciences 30.4; 2018.
Rudy, et al.; "Sentiment Analysis: A Combined Approach"; Journal of Informetrics; 2009.
Chong; "Natural Language Processing for Sentiment Analysis: An Exploratory Analysis on Tweets."; ICAIET; 2014.
Bhuiyan, Hanif et al.; "Retrieving YouTube Video by Sentiment Analysis on User Comment."; ICSIPA; 2017.
Novendri, Risky et al.; "Sentiment Analysis of YouTube Movie Trailer Comments Using Naïve Bayes."; Bulletin of Computer Science and Electrical Engineering 1.1; 2020.
Medhat et al.; "Sentiment Analysis Algorithms and Applications: A Survey."; Ain Shams Engineering Journal 5.4; 2014.
Dang, Nhan Cach, María N. Moreno-García, and Fernando De la Prieta; "Sentiment Analysis Based on Deep Learning: A Comparative Study."; Electronics (Switzerland); 2020.
Phan, Huyen Trang et al.; "Improving the Performance of Sentiment Analysis of Tweets Containing Fuzzy Sentiment Using the Feature Ensemble Model."; IEEE Access 8; 2020.
Gonçalves, Pollyanna et al.; "Comparing and Combining Sentiment Analysis Methods."; COSN 2013 - Association for Computing Machinery; 2013.
Do, Hai Ha et al.; "Deep Learning for Aspect-Based Sentiment Analysis: A Comparative Review."; Expert Systems with Applications; 2019.
Srivastava, Ankit et al.; "Sentiment Analysis of Twitter Data: A Hybrid Approach."; International Journal of Healthcare Information Systems and Informatics; 2019.
Xu, Guixian et al.; "Sentiment Analysis of Comment Texts Based on BiLSTM."; IEEE Access 7; 2019.
Al-Agha, Iyad, and Osama Abu-Dahrooj; "Multi-Level Analysis of Political Sentiments Using Twitter Data: A Case Study of the Palestinian-Israeli Conflict."; Jordanian Journal of Computers and Information Technology; 2019.
Basiri, Mohammad Ehsan et al.; "ABCDM: An Attention-Based Bidirectional CNN-RNN Deep Model for Sentiment Analysis."; Future Generation Computer Systems; 2021.
Vishal A. Kharde et al.; "Sentiment Analysis of Twitter Data: A Survey of Techniques"; International Journal of Computer Applications; 2017.
G. Gautam et al.; "Sentiment analysis of twitter data using machine learning approaches and semantic analysis"; Seventh International Conference on Contemporary Computing (IC3); 2014.
Abdullah Alsaeedi et al.; "A Study on Sentiment Analysis Techniques of Twitter Data"; International Journal of Advanced Computer Science and Applications; 2019.
Acknowledgments
This research is sponsored by Learn By Research Organization, India.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Gangwar, A., Mehta, T. (2023). Sentiment Analysis of Political Tweets for Israel Using Machine Learning. In: Misra, R., Omer, R., Rajarajan, M., Veeravalli, B., Kesswani, N., Mishra, P. (eds) Machine Learning and Big Data Analytics. ICMLBDA 2022. Springer Proceedings in Mathematics & Statistics, vol 401. Springer, Cham. https://doi.org/10.1007/978-3-031-15175-0_15
Download citation
DOI: https://doi.org/10.1007/978-3-031-15175-0_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-15174-3
Online ISBN: 978-3-031-15175-0
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)