2023 Türkiye Cumhurbaşkanlığı Seçimleri için Youtube Yorumlarında Duygu Analizi

Yıl 2023, , 18 - 34, 29.12.2023

Cihan Çılgın

https://doi.org/10.55609/yenimedya.1339272

Öz

13. Cumhurbaşkanlığı seçimi Türkiye’de olduğu kadar birçok ülkede de geniş bir gündem yaratmıştır. Bu seçim sürecinde, seçim kapmanyalarının yürütülmesinde, geleneksel medya araçlarının yanı sıra sosyal medya araçları da çok sık kullanılmıştır. Sosyal medya platformları üzerinden alınan etkileşimler tüm siyasi partilere ve parti yöneticilerine, geniş kitlelere ulaşmak için sosyal medya araçlarının efektif gücünü bir kez daha kanıtlamıştır. Bu nedenle çok sayıda siyasetçinin katılmış olduğu Oğuzhan Uğur tarafından gerçekleştirilen Açık Mikrofon programı sadece Türkiye gündeminde değil tüm dünya gündemi tarafından da ilgi ile takip edilmiştir. Bu kapsamda bu çalışma özellikle bu program kapsamında yapılan yorumlardan Duygu Analizi yöntemleri ile çeşitli analiz bulgularını ortaya koymayı amaçlamaktadır. Bu amaç ile bu çalışma da 7 farklı siyasetçi özelinde toplamda 261.728 kullanıcı yorumu, NRC duygu sözlüğü kullanılarak analiz edilmiştir. NRC duygu sözlüğü ile birlikte pozitif veya negatif duygu polaritesine ek olarak öfke, korku, güven, beklenti, sürpriz, üzüntü, neşe ve tiksinti duygularının da yer aldığı daha geniş bir duygu polaritesi elde edilmiştir. Elde edilen bulgular neticesinde bu çalışma Youtube yorumları veya farklı platformların üzerinden kitlelerin duygu analizinin siyasi kampanyalar için kritik bir bilgi kaynağı olabileceğini ortaya koymaktadır.

Anahtar Kelimeler

Duygu Analizi, Cumhurbaşkanlığı Seçimi, NRC Duygu Sözlüğü, Doğal Dil İşleme, Youtube

Emotion Analysis on Youtube Comments for 2023 Turkish Presidential Elections

Öz

The 13th Presidential election has created a wide agenda in many countries as well as in Turkey. In this election period, along with traditional media tools, social media tools were also used frequently in the execution of election campaigns. Interactions received through social media platforms once again proved the effective power of social media tools to reach large masses of all parties and party leaders. For this reason, the Open Microphone program organized by Oğuzhan Uğur, in which many politicians participated, was followed with interest not only in Turkey's agenda, but also in the world's agenda. In this context, this study aims to reveal various analysis findings with Emotion Analysis methods, especially from the comments made within the scope of this program. For this purpose, in this study, a total of 261.728 user comments, specific to 7 different politicians, were analyzed using the NRC emotion dictionary. With the NRC emotion dictionary, a broader emotional polarity was obtained, including the emotions of anger, fear, trust, anticipation, surprise, sadness, joy, and disgust, in addition to positive or negative emotion polarity. As a result of the findings, this study reveals that the emotion analysis of the masses through Youtube comments or different platforms can be a critical source of information for political campaigns.

Anahtar Kelimeler

Emotion Analysis, Presidential Election, NRC Emotion Dictionary, Natural Language Processing, Youtube

Kaynakça

• Baker Al Barghuthi, N., & E. Said, H. (2020). Sentiment analysis on predicting presidential election: Twitter used case. In Intelligent Computing Systems: Third International Symposium, ISICS 2020, Sharjah, United Arab Emirates, March 18–19, 2020, Proceedings 3 (pp. 105-117). Springer International Publishing.
• Batra, P. K., Saxena, A., & Goel, C. (2020, November). Election result prediction using twitter sentiments analysis. In 2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC) (pp. 182-185). IEEE.
• Bhuiyan, H., Ara, J., Bardhan, R., & Islam, M. R. (2017, September). Retrieving YouTube video by sentiment analysis on user comment. In 2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) (pp. 474-478). IEEE.
• Boutet, A., Kim, H., & Yoneki, E. (2012). What's in your tweets? I know who you supported in the UK 2010 general election. In Proceedings of the International AAAI Conference on Web and Social Media (Vol. 6, No. 1, pp. 411-414). Budiharto, W., & Meiliana, M. (2018). Prediction and analysis of Indonesia Presidential election from Twitter using sentiment analysis. Journal of Big data, 5(1), 1-10.
• Cavnar, W. B., & Trenkle, J. M. (1994, April). N-gram-based text categorization. In Proceedings of SDAIR-94, 3rd annual symposium on document analysis and information retrieval (Vol. 161175, p. 14).
• Ceron, A., Curini, L., & Iacus, S. M. (2015). Using sentiment analysis to monitor electoral campaigns: Method matters—evidence from the United States and Italy. Social Science Computer Review, 33(1), 3-20.
• Cerón-Guzmán, J. A., & León-Guzmán, E. (2016, October). A sentiment analysis system of Spanish tweets and its application in Colombia 2014 presidential election. In 2016 IEEE international conferences on big data and cloud computing (BDCloud), social computing and networking (socialcom), sustainable computing and communications (sustaincom)(BDCloud-socialcom-sustaincom) (pp. 250-257). IEEE.
• Chaudhry, H. N., Javed, Y., Kulsoom, F., Mehmood, Z., Khan, Z. I., Shoaib, U., & Janjua, S. H. (2021). Sentiment analysis of before and after elections: Twitter data of us election 2020. Electronics, 10(17), 2082.
• Choy, P., Sharma, N., & Sikka, G. (2021). The emergence of social media data and sentiment analysis in election prediction. Journal of Ambient Intelligence and Humanized Computing, 12, 2601-2627.
• Chauhan, P., Sharma, N., & Sikka, G. (2023). Application of Twitter sentiment analysis in election prediction: a case study of 2019 Indian general election. Social Network Analysis and Mining, 13(1), 88.
• Choy, M., Cheong, M. L., Laik, M. N., & Shung, K. P. (2011). A sentiment analysis of Singapore Presidential Election 2011 using Twitter data with census correction. arXiv preprint arXiv:1108.5520.
• Cunha, A. A. L., Costa, M. C., & Pacheco, M. A. C. (2019). Sentiment analysis of youtube video comments using deep neural networks. In Artificial Intelligence and Soft Computing: 18th International Conference, ICAISC 2019, Zakopane, Poland, June 16–20, 2019, Proceedings, Part I 18 (pp. 561-570). Springer International Publishing.
• Çılgın, C., BAŞ, M., BİLGEHAN, H., & Unal, C. (2022). Twitter Sentiment Analysis During Covid-19 Outbreak with VADER. AJIT-e Online Academic Journal of Information Technology, 13, 90-106.
• Çılgın, C., Gökçen, H., & Gökşen, Y. (2023). Sentiment analysis of public sensitivity to COVID-19 vaccines on Twitter by majority voting classifier-based machine learning. Journal of the Faculty of Engineering and Architecture of Gazi University, 38(2).
• Endsuy, R. D. (2021). Sentiment analysis between VADER and EDA for the US presidential election 2020 on twitter datasets. Journal of Applied Data Sciences, 2(1), 08-18.
• Gayo-Avello, D. (2012). No, you cannot predict elections with Twitter. IEEE Internet Computing, 16(6), 91-94.
• Gayo-Avello, D. (2013). A meta-analysis of state-of-the-art electoral prediction from Twitter data. Social Science Computer Review, 31(6), 649-679.
• Khoo, C. S., & Johnkhan, S. B. (2018). Lexicon-based sentiment analysis: Comparative evaluation of six sentiment lexicons. Journal of Information Science, 44(4), 491-511.
• Mohammad, S. M., & Kiritchenko, S. (2015). Using hashtags to capture fine emotion categories from tweets. Computational Intelligence, 31(2), 301-326.
• Mohammad, S. M., & Turney, P. D. (2013). Crowdsourcing a word–emotion association lexicon. Computational intelligence, 29(3), 436-465.
• Muhammad, A. N., Bukhori, S., & Pandunata, P. (2019, October). Sentiment analysis of positive and negative of youtube comments using naïve bayes–support vector machine (nbsvm) classifier. In 2019 International Conference on Computer Science, Information Technology, and Electrical Engineering (ICOMITEE) (pp. 199-205). IEEE.
• Nausheen, F., & Begum, S. H. (2018, January). Sentiment analysis to predict election results using Python. In 2018 2nd international conference on inventive systems and control (ICISC) (pp. 1259-1262). IEEE.
• O'Connor, B., Balasubramanyan, R., Routledge, B., & Smith, N. (2010, May). From tweets to polls: Linking text sentiment to public opinion time series. In Proceedings of the international AAAI conference on web and social media (Vol. 4, No. 1, pp. 122-129).
• Oyebode, O., & Orji, R. (2019, October). Social media and sentiment analysis: the Nigeria presidential election 2019. In 2019 IEEE 10th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON) (pp. 0140-0146). IEEE.
• Ramteke, J., Shah, S., Godhia, D., & Shaikh, A. (2016, August). Election result prediction using Twitter sentiment analysis. In 2016 international conference on inventive computation technologies (ICICT) (Vol. 1, pp. 1-5). IEEE.
• Rita, P., António, N., & Afonso, A. P. (2023). Social media discourse and voting decisions influence: sentiment analysis in tweets during an electoral period. Social Network Analysis and Mining, 13(1), 46.
• Salunkhe, P., & Deshmukh, S. (2017). Twitter based election prediction and analysis. International Research Journal of Engineering and Technology, 4(10), 539-544.
• Sanders, E. P., & van Den Bosch, A. P. J. (2013). Relating political party mentions on Twitter with polls and election results.
• Sang, E. T. K., & Bos, J. (2012, April). Predicting the 2011 dutch senate election results with twitter. In Proceedings of the workshop on semantic analysis in social media (pp. 53-60).
• Sharma, P., & Moh, T. S. (2016, December). Prediction of Indian election using sentiment analysis on Hindi Twitter. In 2016 IEEE international conference on big data (big data) (pp. 1966-1971). IEEE.
• Shevtsov, A., Oikonomidou, M., Antonakaki, D., Pratikakis, P., & Ioannidis, S. (2023). What Tweets and YouTube comments have in common? Sentiment and graph analysis on data related to US elections 2020. Plos one, 18(1), e0270542.
• Siersdorfer, S., Chelaru, S., Nejdl, W., & San Pedro, J. (2010, April). How useful are your comments? Analyzing and predicting YouTube comments and comment ratings. In Proceedings of the 19th international conference on World wide web (pp. 891-900).
• Singh, R., & Tiwari, A. (2021). Youtube comments sentiment analysis. International Journal of Scientific Research in Engineering and Management (IJSREM).
• Singh, S., & Sikka, G. (2021, May). YouTube Sentiment Analysis on US Elections 2020. In 2021 2nd International Conference on Secure Cyber Computing and Communications (ICSCCC) (pp. 250-254). IEEE.
• Taboada, M., Brooke, J., Tofiloski, M., Voll, K., & Stede, M. (2011). Lexicon-based methods for sentiment analysis. Computational linguistics, 37(2), 267-307.
• Tumasjan, A., Sprenger, T., Sandner, P., & Welpe, I. (2010, May). Predicting elections with twitter: What 140 characters reveal about political sentiment. In Proceedings of the international AAAI conference on web and social media (Vol. 4, No. 1, pp. 178-185).
• Uysal, E., Yumusak, S., Oztoprak, K., & Dogdu, E. (2017, April). Sentiment analysis for the social media: A case study for turkish general elections. In Proceedings of the SouthEast Conference (pp. 215-218).
• Wisnubroto, A. S., Saifunas, A., Santoso, A. B., Putra, P. K., & Budi, I. (2022, December). Opinion-based sentiment analysis related to 2024 Indonesian Presidential Election on YouTube. In 2022 5th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI) (pp. 318-323). IEEE.
• Yavari, A., Hassanpour, H., Rahimpour Cami, B., & Mahdavi, M. (2022). Election prediction based on sentiment analysis using twitter data. International Journal of Engineering, 35(2), 372-379.