Sentiment Analysis in Turkish Tweets Using Different Machine Learning Algorithms

Year 2024, Volume: 4 Issue: 2, 107 - 120, 01.10.2024

Hunaıda Avvad , Ecem Ereren

Abstract

Understanding emotions in any written text is considered as a hot topic for many researchers in the field of text mining, especially with the large contribution of users over the web 2.0 and with the growth of the different social media platforms. In this study we analysed emotions on Turkish text and studied the sentiment within each document using Sentiment Analysis techniques. Sentiment Analysis is the process of identifying and evaluating the emotional states contained in texts. This study aimed to investigate the effect and accuracy rate of sentiment analysis in Turkish texts. Sentiment analysis is an important field of research that helps to obtain important data in many areas such as marketing, social media analysis, and customer feedback. A comprehensive data set consisting of Turkish tweets from Kaggle was used and the emotional states of the texts were labelled. This data set consists of a variety of tweets with different topics and emotional tones. Using natural language processing techniques and machine learning algorithms, the data set was processed, and the model was trained. Within the scope of the study, different root extraction methods and a vector space model were used. In addition, machine learning algorithms such as Naive Bayes, Random Forest, Decision Tree, Gradient Boosting, Bernoulli Naive Bayes, Logistic Regression, K-Neighbours-Classifier, and Support Vector Classifier were applied to evaluate accuracy. This study aims to emphasize the importance of sentiment analysis in Turkish texts, to examine the impact of the methods used and to form a basis for future studies.

Keywords

sentiment analysis, Turkish text, machine learning, Turkish tweet

References

• [1] Tokcaer, S. (2021). Türkçe metinlerde duygu analizi. Yaşar University E-Dergisi, 16(63), 1514-1534.
• [2] Kaynar, O., Görmez, Y., Yıldız, M., & Albayrak, A. (2016, September). Makine öğrenmesi yöntemleri ile Duygu Analizi. In International Artificial Intelligence and Data Processing Symposium (IDAP'16) (Vol. 17, No. 18, pp. 17-18).‏
• [3] Danisman, T., & Alpkocak, A. (2008, April). Feeler: Emotion classification of text using vector space model. In AISB 2008 convention communication, interaction and social intelligence (Vol. 1, p. 53).‏ T.
• [4] Medler, D. A., Arnoldussen, A., Binder, J.R., & Seidenberg, M.S. (2005). The Wisconsin Perceptual Attribute Ratings Database. http://www.neuro.mcw.edu/ratings/
• [5] Alpkoçak, A., Tocoglu, M. A., Çelikten, A., & Aygün, İ. (2019). Türkçe metinlerde duygu analizi için farklı makine öğrenmesi yöntemlerinin karşılaştırılması. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi, 21(63), 719-725.‏
• [6] Türkmenoğlu, C. (2016). Türkçe metinlerde duygu analizi (Doctoral dissertation, Fen Bilimleri Enstitüsü).
• [7] Kozareva, Z., Navarro, B., Vázquez, S., & Montoyo, A. (2007, June). UA-ZBSA: a headline emotion classification through web information. In Proceedings of the Fourth International Workshop on Semantic Evaluations (SemEval-2007) (pp. 334-337).‏
• [8] Mohammad, S. (2012, June). Portable features for classifying emotional text. In Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (pp. 587-591).‏
• [9] Chaffar, S., & Inkpen, D. (2011). Using a heterogeneous dataset for emotion analysis in text. In Advances in Artificial Intelligence: 24th Canadian Conference on Artificial Intelligence, Canadian AI 2011, St. John’s, Canada, May 25-27, 2011. Proceedings 24 (pp. 62-67). Springer Berlin Heidelberg.‏
• [10] Boynukalın, Z. (2012). Emotion analysis of Turkish texts by using machine learning methods (Master's thesis, Middle East Technical University).‏
• [11] Kaya, M., Fidan, G., & Toroslu, I. H. (2012, December). Sentiment analysis of Turkish political news. In 2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology (Vol. 1, pp. 174-180). IEEE.‏
• [12] Oflazer, K. (1994). Two-level description of Turkish morphology. Literary and linguistic computing, 9(2), 137-148.‏
• [13] Neviarouskaya, A., Prendinger, H., & Ishizuka, M. (2007, January). Analysis of affect expressed through the evolving language of online communication. In Proceedings of the 12th international conference on Intelligent user interfaces (pp. 278-281).‏
• [14] Wang, W., Chen, L., Thirunarayan, K., & Sheth, A. P. (2012, September). Harnessing twitter" big data" for automatic emotion identification. In 2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing (pp. 587-592). IEEE.‏
• [15] U. Erogul. Sentiment analysis in Turkish. Master’s thesis, Middle East Technical University, 2009.
• [16] Pang, B., Lee, L., & Vaithyanathan, S. (2002). Thumbs up? Sentiment classification using machine learning techniques. arXiv preprint cs/0205070.‏
• [17] O’Connor, K., Pimpalkhute, P., Nikfarjam, A., Ginn, R., Smith, K. L., & Gonzalez, G. (2014). Pharmacovigilance on twitter? Mining tweets for adverse drug reactions. In AMIA annual symposium proceedings (Vol. 2014, p. 924). American Medical Informatics Association.‏
• [18] Alawi, A. B., & Bozkurt, F. (2024). A hybrid machine learning model for sentiment analysis and satisfaction assessment with Turkish universities using Twitter data. Decision Analytics Journal, 11, 100473.
• [19] Cam, H., Cam, A. V., Demirel, U., & Ahmed, S. (2024). Sentiment analysis of financial Twitter posts on Twitter with the machine learning classifiers. Heliyon, 10(1).
• [20] İnan, H. E. Comparison of Machine Learning Algorithms for Classification of Hotel Reviews: Sentiment Analysis of TripAdvisor Reviews. GSI Journals Serie A: Advancements in Tourism Recreation and Sports Sciences, 7(1), 111-122.
• [21] Alzoubi, Y. I., Topcu, A. E., & Erkaya, A. E. (2023). Machine learning-based text classification comparison: Turkish language context. Applied Sciences, 13(16), 9428.