Detection of Americans? Behavior toward Islam on Facebook
DOI:
https://doi.org/10.5614/itbj.ict.res.appl.2022.16.3.7Keywords:
analysis, behavior, document frequency, frequency-inverse, logistic regession, sentiment analysisAbstract
Social network websites have become a rich place for detecting and analyzing people?s attitudes, perceptions, and feelings towards news, products, and other real-world issues. Facebook is a popular platform among different age groups and countries and is generally used to convey ideas about certain topics based on likes, comments and sharing. In recent years, one of the most controversial topics were the idea behind Islamophobia and other ideas built in people?s minds about Islam around the world. This research studied the public opinion of American citizens about Islam during the presidency of Donald Trump, as that period was rich in diversity of opinion between his supporters and detractors. In this paper, sentiment analysis was used to analyze American citizens? behavior towards posts about Islam during Trump?s presidency in various states across the United States. Sentiment analysis was performed on Facebook posts and comments extracted from American news channels from the year 2017. Several machine learning methods were used to detect the polarity in the dataset. The highest classification accuracy among the classifiers used in this research was achieved using a logistic regression classifier, reaching 84%.
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References
Haque, F., Orthy, A.H. & Siddique, S., Hadith Authenticity Prediction using Sentiment Analysis and Machine Learning, 2020 IEEE 14th International Conference on Application of Information and Communication Technologies (AICT), Uzbekistan, Tashkent, 07-09 October 2020, pp. 1-6, 2020. DOI: 10.1109/AICT50176.2020.9368569.
Yassine, A., Mohamed, L., Kadiri, E. & Eddine, K., Random Forest and Support Vector Machine based Hybrid Approach to Sentiment Analysis Procedia Computer Science, 127, pp. 511-520, 2018. DOI: 10.1016/j.procs.2018.01.150.
Farha, N. & Begum, S.H., Sentiment Analysis to Predict Election Results using Python, 2018 2nd International Conference on Inventive Systems and Control (ICISC), Coimbatore, India , pp. 1259-1262, 2018. DOI:10.1109/ICISC.2018.8399007.
Statista Research Department, Cumulative Number of Monthly Facebook Product Users as of 3rd Quarter, Facebook Global Community MAU 2021, Nov 1, 2021.
Alraddadi, R.A., Ibrahim, M. & Ghembaza, E.K., Anti-Islamic Arabic Text Categorization using Text Mining and Sentiment Analysis Techniques, (IJACSA) International Journal of Advanced Computer Science and Applications, 12(5), pp. 776-785, 2021.
Harshal P. & Arun, P., Rajendra, T., Punam, P. & Ospanova, A., Sentiment Analysis of Text Feedback, International Journal of Innovative Research in Science Engineering and Technology, 11, pp. 3485-3491, 2022. DOI: 10.15680/IJIRSET.2022.1104052.
Tiwari, S., Verma, A., Garg, P. & Bansal, D., Social Media Sentiment Analysis on Twitter Datasets, 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS), pp. 925-927, 2020. DOI: 10.1109/ICACCS48705.2020.9074208.
El Rahman, S.A., AlOtaibi, F.A. & AlShehri, W.A., Sentiment Analysis of Twitter Data, 2019 International Conference on Computer and Information Sciences (ICCIS), 2019. DOI:10.1109/iccisci.2019.87164.
Al-Hadhrami, S., Al-Fassam, N. & Benhidour, H., Sentiment Analysis of English Tweets: A Comparative Study of Supervised and Unsupervised Approaches, 2019 2nd International Conference on Computer Applications & Information Security (ICCAIS), pp. 1-5, 2019. DOI: 10.1109/CAIS.2019.8769550.
Bencina, J., Facebook News Scraper GitHub - jbencina/facebook-news, 2017.
Birjali, M., Kasri, M. & Beni-Hssane, A., A Comprehensive Survey on Sentiment Analysis: Approaches, Challenges and Trends, Knowledge-Based Systems, 226, 107134, 2021. DOI: 10.1016/j.knosys.2021.107134.
Yeong Hyeon, G., Seong Joon, Y., Zhiyan, J., Yeo Jin, L., Zhegao, P., Helin, Y. & Seogbong, J., Sentiment Analysis and Visualization of Chinese Tourism Blogs and Reviews, IEEE 2018 International Conference on Electronics, Information, and Communication (ICEIC) - Honolulu, HI (2018.1.24-2018.1.27), pp. 1-4, 2018. DOI:10.23919/ELINFOCOM.2018.8330589.
Sultana, N. & Islam, M.M., Meta Classifier-Based Ensemble Learning for Sentiment Classification, Algorithms for Intelligent Systems, pp. 73-84, 2019. DOI:10.1007/978-981-13-7564-4_7.
Nikhil, Y., Omkar, K., Srishti, G., Aditi, R. & Ajitkumar, S., Twitter Sentiment Analysis using Supervised Machine Learning, in Hemanth, J., Bestak, R. & Chen, J.I-Z., Intelligent Data Communication Technologies and Internet of Things, pp. 631-642, 2020.
Bolbol, N.K. & Maghari, A.Y., Sentiment Analysis of Arabic Tweets Using Supervised Machine Learning, 2020 International Conference on Promising Electronic Technologies (ICPET). DOI:10.1109/icpet51420.2020.00025.
Ankit & Saleena, N.., An Ensemble Classification System for Twitter Sentiment Analysis, Procedia Computer Science, 132, pp. 937-946, 2018. DOI: 10.1016/j.procs.2018.05.109.
Jain, P.K., Pamula, R. & Srivastava, G., A Systematic Literature Review on Machine Learning Applications for Consumer Sentiment Analysis Using Online Reviews. Computer Science Review, 41, 100413, 2021. DOI: 10.1016/j.cosrev.2021.100413.
