Automatic Movie Rating by Using Twitter Sentiment Analysis and Monitoring Tool

Abstract

Today, due to the intense use of social media platforms such as Twitter by all segments of today's technology, people have begun to share their views, ideas, and feelings through these media. It is possible to discover mighty valuable knowledge from this enormous resource. This study has emerged to assist users in making choices by evaluating emotions about TV series and movies that have recently appeared on social platforms, using ideas and feelings. The textual tweet data was preprocessed and cleaned of noise by using natural language processing techniques. Tweets were tagged using the Bert-based model according to the content of the Turkish TV series and movie comments, and their polarities were calculated. Machine learning models including Naïve Bayes (NB), Support Vector Machines (SVM), Random Forest (RF); Bagging and Voting, which are among the general ensemble algorithms, were trained for sentiment analysis by taking the obtained polarity values. The voting algorithm gives the best accuracy at 87%, while the Support Vector Machines give the best area under the receiver operating characteristics curve (AUC) of 0.96. A web application was developed by using Flask to monitor sentiment scores via hashtags (#).

Keywords

• Sentiment Analysis
• Machine Learning
• Natural Language Processing
• Social Data Science

References

1. S. Tuzcu, “Çevrimiçi Kullanıcı Yorumlarının Duygu Analizi ile Sınıflandırılması,” Eskişehir Türk Dünyası Uygulama ve Araştırma Merkezi Bilişim Dergisi, 1(2), 1-5, 2020.
2. D. Zimbra, A. Abbasi, D. Zeng, and H. Chen, “The state-of-the-art in Twitter sentiment analysis: A review and benchmark evaluation,” ACM Transactions on Management Information Systems (TMIS) 9.2, 2018, 1-29.
3. H. Wang, D. Can, A. Kazemzadeh, F. Bar, and S. Narayanan, “A system for real-time Twitter sentiment analysis of 2012 us presidential election cycle,” In Proceedings of the ACL 2012 system demonstrations, pp. 115-120, July 2012.
4. G. Abalı, E. Karaarslan, A. Hürriyetoğlu, and F. Dalkılıç, “Detecting citizen problems and their locations using twitter data,” In Proceedings of the 6th International Istanbul Smart Grids and Cities Congress and Fair (ICSG), 2018, pp. 30-33, doi: 10.1109/SGCF.2018.8408936.
5. K. Chakraborty, S. Bhatia, S. Bhattacharyya, J. Platos, R. Bag, and A. E. Hassanien, “Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers—A study to show how popularity is affecting accuracy in social media,” Applied Soft Computing, 97, 106754, 2020.
6. D. R. Pant, P. Neupane, A. Poudel, A. K. Pokhrel, and B. K. Lama, “Recurrent neural network based bitcoin price prediction by twitter sentiment analysis,” In Proceedings of the IEEE 3rd International Conference on Computing, Communication and Security (ICCCS), pp. 128-132, October 2018.
7. S. E. Shukri, R. I. Yaghi, I. Aljarah, and H. Alsawalqah, “Twitter sentiment analysis: A case study in the automotive industry” In Proceedings of the IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT), IEEE, pp. 1-5, November 2015.
8. C. Quan and F. Ren, “Unsupervised product feature extraction for feature-oriented opinion determination,” Information Sciences, 272, 16-28, 2014.

9. S. Atan and Y. Çınar, “Borsa İstanbul’da finansal haberler ile piyasa değeri ilişkisinin metin madenciliği ve duygu (sentiment) analizi ile incelenmesi,” Ankara Üniversitesi SBF Dergisi, 74.1, pp. 1-34, 2019.
10. B. Karagöz and U. T. Gürsoy, “Adaptif Öğrenme Sözlüğü Temelli Duygu Analiz Algoritması Önerisi,” International Journal of Informatics Technologies, 11(3), pp. 245-253, 2018.
11. B. Pang, L. Lee, and S. Vaithyanathan, “Thumbs up? Sentiment classification using machine learning techniques,” arXiv preprint, cs/0205070, 2002.
12. R. Li, K. H. Lei, R. Khadiwala, and K. C. C. Chang, “Tedas: A twitter-based event detection and analysis system,” In Proceedings of the IEEE 28th International Conference on Data Engineering, pp. 1273-1276, April 2012.
13. M.E. Basiri, S. Nemati, M. Abdar, S. Asadi, and U.R. Acharrya, “A novel fusion-based deep learning model for sentiment analysis of COVID-19 tweets,” Knowledge-Based Systems, 228 (2021): 107242.
14. M. J. D. Torres, “Contributions to Social Learning Analytics based on Sentiment Analysis of Students Interactions in Educational Environments,” Doctoral dissertation, Universidad De Las Américas Puebla, 2019.
15. O. Kaynar, Y. Görmez, M. Yıldız, and A. Albayrak, “Makine öğrenmesi yöntemleri ile duygu analizi,” In Proceedings of the International Artificial Intelligence and Data Processing Symposium, pp. 17-18, 2016.
16. R. Patel and K. Passi, “Sentiment analysis on Twitter data of world cup soccer tournament using machine learning,” IoT, 1(2), pp. 218-239, 2020.
17. MongoDB (2021, December 18), Retrieved from https://www.mongodb.com/.
18. J. Devlin, M. W. Chang, K. Lee, and K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding,” arXiv preprint arXiv:1810.04805, 2018.