Sentiment analysis of news videos about artificial intelligence in Turkey: A YouTube case
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Introduction: Debate on the future of Artificial Intelligence (AI) has recently been polarized. Positive, negative, and neutral differences of opinion about AI have led to the need for further inquiry into this issue. In particular, establishing AI’s future potential of use by identifying the feelings and opinions of countries about AI is deemed significant for developing nationwide and regional AI strategies. In this regard, this study aimed to determine the emotional states of Turks toward AI. Social media platform was accordingly exploited since it is an important data source to determine individuals’ feelings and opinions. Methodology: User comments on the posts published by Turkish national news channels on YouTube were examined through Sentiment Analysis (SA). In the dictionary-based SA method implemented, consumer/follower comments were classified as positive, neutral, and negative according to their polarity scores. Results: Analyses indicated that 697 (48.6%) of user comments were positive, 380 (26.5%) were negative, and 357 (24.9%) were neutral. It was concluded that Turkish society’s feelings toward AI were generally positive. Discussion and Conclusions: YouTube users' current emotional states, with or without knowledge of artificial intelligence, may differ in the future. It might thus be viewed as predictable that in the future, users who are more positioned in these processes will experience certain shifts in their sentiment states toward specific issues, from positive to negative, from negative to positive, or from neutral sentimental states to positive or negative.
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Agarwal, A., Biadsy, F., & Mckeown, K. (2009). Contextual phrase-level polarity analysis using lexical affect scoring and syntactic n-grams. In Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics (EACL '09) (pp. 24-32). Association for Computational Linguistics. https://doi.org/10.3115/1609067.1609069 DOI: https://doi.org/10.3115/1609067.1609069
Aslan, S. (2023). Doğal dil işleme teknikleri kullanarak e-ticaret kullanıcı incelemelerinde özellik tabanlı duygu analizi. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 35(2), 875-882. https://doi.org/10.35234/fumbd.1335583 DOI: https://doi.org/10.35234/fumbd.1335583
Bartneck, C., Suzuki, T., Kanda, T., & Nomura, T. (2007). The influence of people's culture and prior experiences with Aibo on their attitude towards robots. Ai & Society, 21, 217-230. https://doi.org/10.1007/s00146-006-0052-7 DOI: https://doi.org/10.1007/s00146-006-0052-7
Benyon, D., Turner, P., & Turner S. (2005). Designing interactive systems. Pearson Education.
Borenstein, J. (2011). Robots and the changing workforce. AI & Society, 26, 87-93. https://doi.org/10.1007/s00146-009-0227-0 DOI: https://doi.org/10.1007/s00146-009-0227-0
Brauner, P., Hick, A., Philipsen, R., & Ziefle, M. (2023). What does the public think about artificial intelligence?—A criticality map to understand bias in the public perception of AI. Frontiers in Computer Science, 5. https://doi.org/10.3389/fcomp.2023.1113903 DOI: https://doi.org/10.3389/fcomp.2023.1113903
Bryson, J. J. (2019). The past decade and future of AI's impact on society. In M. Baddeley (Ed.), Towards a new enlightenment? A transcendent decade. (Vol. 11). Turner. https://www.bbvaopenmind.com/wp-content/uploads/2019/02/BBVA-OpenMind-Joanna-J-Bryson-The-Past-Decade-and-Future-of-AI-Impact-on-Society.pdf
Chauhan, V. K., Bansal, A., & Goel, D. A. (2018). Twitter sentiment analysis using vader. International Journal of Advance Research, Ideas and Innovations in Technology (IJARIIT), 4(1), 485-489. https://www.ijariit.com/manuscripts/v4i1/V4I1-1307.pdf
Cheung, C. W., Tsang I. T., & Wong, K. H. (2017). Robot Avatar: A Virtual Tourism Robot for People with Disabilities. International Journal of Computer Theory and Engineering, Singapore, 9(3), 229-234. https://doi.org/10.7763/IJCTE.2017.V9.1143 DOI: https://doi.org/10.7763/IJCTE.2017.V9.1143
Cohen, M., Khavkin, M., Movsowitz Davidow, D., & Toch, E. (2024). ChatGPT in the public eye: Ethical principles and generative concerns in social media discussions. New Media & Society. https://doi.org/10.1177/14614448241279034 DOI: https://doi.org/10.1177/14614448241279034
Diallo, S. Y., Shults, F. L., & Wildman, W. J. (2021). Minding morality: ethical artificial societies for public policy modeling. AI & society, 36(1), 49-57. https://doi.org/10.1007/s00146-020-01028-5 DOI: https://doi.org/10.1007/s00146-020-01028-5
Dierbach, C. (2012). Introduction to computer science using python: A computational problem-solving focus. Wiley Publishing.
Dilek, Ö. G. (2019). Yapay zekanın etik gerçekliği. Ankara Uluslararası Sosyal Bilimler Dergisi, 2(4), 47-59. https://dergipark.org.tr/tr/pub/usdad/issue/51335/642184
Efe, A. (2021). Yapay Zekâ Risklerinin Etik Yönünden Değerlendirilmesi. Bilgi ve İletişim Teknolojileri Dergisi, 3(1), 1-24. https://dergipark.org.tr/tr/pub/bited/issue/63346/859894
Ercan, F. (2020). Turizm pazarlamasında yapay zekâ teknolojilerinin kullanımı ve uygulama örnekleri. Ankara Hacı Bayram Veli Üniversitesi Turizm Fakültesi Dergisi, 23(2), 394-410. https://doi.org/10.34189/tfd.23.02.009 DOI: https://doi.org/10.34189/tfd.23.02.009
Fast, E., & Horvitz, E. (2017, February). Long-Term Trends in the Public Perception of Artificial Intelligence. Proceedings of the AAAI Conference on Artificial Intelligence, 31(1). https://doi.org/10.1609/aaai.v31i1.10635 DOI: https://doi.org/10.1609/aaai.v31i1.10635
Fazzin, S. (2019) The Future of Emotions in the Workplace: The Role of Artificial Intelligence in Modern Personnel Management. In Emotion-Based Approaches to Personnel Management: Emerging Research and Opportunities. IGI Global. https://doi.org/10.4018/978-1-5225-8398-1.ch009 DOI: https://doi.org/10.4018/978-1-5225-8398-1.ch009
Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation?. Technological Forecasting and Social Change, 114, 254-280. https://doi.org/10.1016/j.techfore.2016.08.019 DOI: https://doi.org/10.1016/j.techfore.2016.08.019
Gezgin, U. B. (2023). Yapay Zeka ve Toplum: Yapay Zeka Sosyolojisiyle Eleştirel Bir Bakış. In Yapay Zeka Psikolojisi ve Sosyolojisi (pp. 7-26). Serüven. https://hdl.handle.net/20.500.12941/182
Ghotbi, N., Ho, M. T., & Mantello, P. (2022). Attitude of college students towards ethical issues of artificial intelligence in an international university in Japan. AI & Society, 37, 283-290. https://doi.org/10.1007/s00146-021-01168-2 DOI: https://doi.org/10.1007/s00146-021-01168-2
Google Colab. (s.f.). Te damos la bienvenida a Colab. https://colab.research.google.com/
Hemmatian, F., & Sohrabi, M. K. (2019). A survey on classification techniques for opinion mining and sentiment analysis. Artificial Intelligence Review, 52, 1495-1545. https://doi.org/10.1007/s10462-017-9599-6 DOI: https://doi.org/10.1007/s10462-017-9599-6
Ho, M. T., Le, N. T. B., Mantello, P., Ho, M. T., & Ghotbi, N. (2023). Understanding the acceptance of emotional artificial intelligence in Japanese healthcare system: A cross-sectional survey of clinic visitors’ attitude. Technology in Society, 72, 102166. https://doi.org/10.1016/j.techsoc.2022.102166 DOI: https://doi.org/10.1016/j.techsoc.2022.102166
Hutto, C., & Gilbert, E. (2014). VADER: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text. Proceedings of the International AAAI Conference on Web and Social Media, 8(1), 216-225. https://doi.org/10.1609/icwsm.v8i1.14550 DOI: https://doi.org/10.1609/icwsm.v8i1.14550
Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389-399. https://doi.org/10.1038/s42256-019-0088-2 DOI: https://doi.org/10.1038/s42256-019-0088-2
Johansson, J. V., Dembrower, K., Strand, F., & Grauman, Å. (2024). Women's perceptions and attitudes towards the use of AI in mammography in Sweden: a qualitative interview study. BMJ Open, 14(2), e084014. https://doi.org/10.1136/bmjopen-2024-084014 DOI: https://doi.org/10.1136/bmjopen-2024-084014
Ju, W., & Takayama L. (2011). Should robots or people do these jobs? A survey of robotics experts and non-experts about which jobs robots should do. In 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (pp. 2452-2459). https://doi.org/10.1109/IROS.2011.6094759 DOI: https://doi.org/10.1109/IROS.2011.6094759
Kamppuri, M., Bednarik, R., & Tukiainen, M. (2006). The expanding focus of HCI: case culture. In A. Mørch, K. Morgan, T. Bratteteig, G. Ghosh, D. Svanaes (Eds.), NordiCHI '06: Proceedings of the 4th Nordic conference on Human-computer interaction: changing roles (pp. 405-408). Association for Computing Machinery. https://doi.org/10.1145/1182475.1182523 DOI: https://doi.org/10.1145/1182475.1182523
Kaur, C., & Sharma, A. (2020). Social issues sentiment analysis using python. In 2020 5th international conference on computing, communication and security (ICCCS) (pp. 1-6). IEEE. https://doi.org/10.1109/ICCCS49678.2020.9277251 DOI: https://doi.org/10.1109/ICCCS49678.2020.9277251
Khan, F. H., Qamar, U., & Bashir, S. (2017). Lexicon based semantic detection of sentiments using expected likelihood estimate smoothed odds ratio. Artificial Intelligence Review, 48, 113-138. https://doi.org/10.1007/s10462-016-9496-4 DOI: https://doi.org/10.1007/s10462-016-9496-4
Ko, J., & Song, A. (2024). Youth perceptions of AI ethics: a Q methodology approach. Ethics & Behavior, 1-18. https://doi.org/10.1080/10508422.2024.2396582 DOI: https://doi.org/10.1080/10508422.2024.2396582
Li, H., Chen, Q., Zhong, Z., Gong, R., & Han, G. (2022). E-word of mouth sentiment analysis for user behavior studies. Information Processing & Management, 59(1), 102784. https://doi.org/10.1016/j.ipm.2021.102784 DOI: https://doi.org/10.1016/j.ipm.2021.102784
Liu B., & Zhang, L. (2012). A Survey of Opinion Mining and Sentiment Analysis. In C. Aggarwal and C. Zhai (Eds.), Mining text data (pp. 415-463). Springer. https://doi.org/10.1007/978-1-4614-3223-4_13 DOI: https://doi.org/10.1007/978-1-4614-3223-4_13
Liu, B. (2012). Sentiment analysis and opinion mining. In Synthesis Lectures on Human Language Technologies. Springer Nature. https://doi.org/10.1007/978-3-031-02145-9 DOI: https://doi.org/10.1007/978-3-031-02145-9
Lohr, S. (January 12, 2017). Robots Will Take Jobs, but Not as Fast as Some Fear, New Report Says. The New York Times. https://www.nytimes.com/2017/01/12/technology/robots-will-take-jobs-but-not-as-fast-as-some-fear-new-report-says.html
Lohr, S., & Markoff, J. (June 24, 2010). Computers Learn to Listen, and Some Talk Back. The New York Times. https://www.nytimes.com/2010/06/25/science/25voice.html
Markoff, J. (December 15, 2014). Study to Examine Effects of Artificial Intelligence. The New York Times. https://www.nytimes.com/2014/12/16/science/century-long-study-will-examine-effects-of-artificial-intelligence.html
McCarthy, J., Minsky, M., Rochester, N., & Shannon, C. (1955). A proposal for the Dartmouth Summer Research Project on Artificial Intelligence. http://wwwformal.stanford.edu/jmc/history/dartmouth.pdf
Medhat, W., Hassan, A., & Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal, 5(4), 1093-1113. https://doi.org/10.1016/j.asej.2014.04.011 DOI: https://doi.org/10.1016/j.asej.2014.04.011
Nausheen, F., & Begum, S. H. (2018). Sentiment analysis to predict election results using Python. In 2018 2nd international conference on inventive systems and control (ICISC) (pp. 1259-1262). IEEE. https://doi.org/10.1109/ICISC.2018.8399007 DOI: https://doi.org/10.1109/ICISC.2018.8399007
Nezhad, Z. B., & Deihimi, M. A. (2022). Twitter sentiment analysis from Iran about COVID 19 vaccine. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 16(1), 102367. https://doi.org/10.1016/j.dsx.2021.102367 DOI: https://doi.org/10.1016/j.dsx.2021.102367
Onan, A., & Korukoğlu, S. (2016). A review of literature on the use of machine learning methods for opinion mining. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 22(2), 111-122. https://doi.org/10.5505/pajes.2015.90018 DOI: https://doi.org/10.5505/pajes.2016.90018
Onan, A., Korukoğlu, S., & Bulut, H. (2016). A multiobjective weighted voting ensemble classifier based on differential evolution algorithm for text sentiment classification. Expert Systems with Applications, 62, 1-16. https://doi.org/10.1016/j.eswa.2016.06.005 DOI: https://doi.org/10.1016/j.eswa.2016.06.005
Palanisamy, P., Yadav, V., & Elchuri, H. (2013, June). Serendio: Simple and Practical lexicon based approach to Sentiment Analysis. In Second Joint Conference on Lexical and Computational Semantics (* SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (pp. 543-548). https://aclanthology.org/S13-2091/
Pang, B., Lee, L., & Vaithyanathan, S. (2002). Thumbs up?: Sentiment Classification Using Machine Learning Techniques. In Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10 (EMNLP '02) (pp. 79-86). Association for Computational Linguistics. https://doi.org/10.3115/1118693.1118704 DOI: https://doi.org/10.3115/1118693.1118704
Pasquale, F. (2015). The Black Box Society: The Secret Algorithms That Control Money and Information. Harvard University Press. https://doi.org/10.4159/harvard.9780674736061 DOI: https://doi.org/10.4159/harvard.9780674736061
Pawar, K. K., Shrishrimal, P. P., & Deshmukh, R. R. (2015). Twitter Sentiment Analysis: A Review. International Journal of Scientific & Engineering Research, 6(4), 957-964. https://www.researchgate.net/profile/Kishori-Pawar/publication/277554643_Twitter_Sentiment_Analysis_A_Review/links/556c64a008aec22683049811/Twitter-Sentiment-Analysis-A-Review.pdf
Pedersen, T., & Johansen, C. (2020). Behavioural artificial intelligence: an agenda for systematic empirical studies of artificial inference. AI & Society, 35, 519-532. https://doi.org/10.1007/s00146-019-00928-5 DOI: https://doi.org/10.1007/s00146-019-00928-5
Qian, C., Mathur, N., Zakaria, N. H., Arora, R., Gupta, V., & Ali, M. (2022). Understanding public opinions on social media for financial sentiment analysis using AI-based techniques. Information Processing & Management, 59(6), 103098. https://doi.org/10.1016/j.ipm.2022.103098 DOI: https://doi.org/10.1016/j.ipm.2022.103098
Riaz, S., Fatima, M., Kamran, M., & Nisar, M. W. (2019). Opinion mining on large scale data using sentiment analysis and k-means clustering. Cluster Computing, 22(Suppl 3), 7149-7164. https://doi.org/10.1007/s10586-017-1077-z DOI: https://doi.org/10.1007/s10586-017-1077-z
Riley, C., Buckner, K., Johnson, G., & Benyon, D. (2009). Culture & biometrics: regional differences in the perception of biometric authentication technologies. AI & Society, 24(3), 295-306. https://doi.org/10.1007/s00146-009-0218-1 DOI: https://doi.org/10.1007/s00146-009-0218-1
Sindermann, C., Sha, P., Zhou, M., Wernicke, J., Schmitt, H. S., Li, M., Sariyska, R., Stavrou, M., Becker, B., & Montag, C. (2021). Assessing the Attitude Towards Artificial Intelligence: Introduction of a Short Measure in German, Chinese, and English Language. KI-Künstliche Intelligenz, 35, 109-118. https://doi.org/10.1007/s13218-020-00689-0 DOI: https://doi.org/10.1007/s13218-020-00689-0
Soler, J. M., Cuartero, F., & Roblizo, M. (2012). Twitter as a tool for predicting elections results. In 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (pp. 1194-1200). IEEE. https://doi.org/10.1109/ASONAM.2012.206 DOI: https://doi.org/10.1109/ASONAM.2012.206
Stine, R. A. (2019). Sentiment analysis. Annual review of statistics and its application, 6, 287-308. https://doi.org/10.1146/annurev-statistics-030718-105242 DOI: https://doi.org/10.1146/annurev-statistics-030718-105242
Tan, S., & Zhang, J. (2008). An empirical study of sentiment analysis for chinese documents. Expert Systems with Applications, 34(4), 2622-2629. https://doi.org/10.1016/j.eswa.2007.05.028 DOI: https://doi.org/10.1016/j.eswa.2007.05.028
Tong, R. M. (2001). An Operational System for Detecting and Tracking Opinions in On-Line Discussion. In Proceedings of SIGIR 2001 Workshop on Operational Text Classification. ABD.
Tsaih, R.-H., & Hsu, C. C. (2018). Artificial intelligence in smart tourism: a conceptual framework. In Proceedings of The 18th International Conference on Electronic Business (pp. 124-133). ICEB. https://iceb.johogo.com/proceedings/2018/ICEB2018_paper_84_full.pdf
Turney, P. D. (2002). Thumbs up or Thumbs down?: Semantic Orientation Applied to Unsupervised Classification of Reviews. In Proceedings of the 40th Annual Meeting on Association for Computational Linguistics (ACL '02) (pp. 417-424). Association for Computational Linguistics. https://doi.org/10.3115/1073083.1073153 DOI: https://doi.org/10.3115/1073083.1073153
Vasileios, H., & Janyce, M. W. (2000). Effects of Adjective Orientation and Gradability on Sentence Subjectivity. In Proceedings of the 18th conference on Computational linguistics - Volume 1 (COLING '00) (pp. 299-305). Association for Computational Linguistics. https://doi.org/10.3115/990820.990864 DOI: https://doi.org/10.3115/990820.990864
Williams, W., Parkes, E. L., & Davies, P. (2013). Wordle: A method for analysing MBA student induction experience. The International Journal of Management Education, 11(1), 44-53. https://doi.org/10.1016/j.ijme.2012.10.002 DOI: https://doi.org/10.1016/j.ijme.2012.10.002
Wilson, T., Wiebe, J., & Hoffmann, P. (2005, October). Recognizing contextual polarity in phrase-level sentiment analysis. In Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing (HLT '05) (pp. 347-354). Association for Computational Linguistics. https://doi.org/10.3115/1220575.1220619 DOI: https://doi.org/10.3115/1220575.1220619
Yaşa, H. (2022). Çevre(cilik) Hareketi Olarak Sosyal Medyada Sıfır Atık Hareketi. Selçuk Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 49, 212-230. https://doi.org/10.52642/susbed.1156189 DOI: https://doi.org/10.52642/susbed.1156189
Zhang, B., & Dafoe, A. (2019). Artificial Intelligence: American Attitudes and Trends. SSRN. https://doi.org/10.2139/ssrn.3312874 DOI: https://doi.org/10.2139/ssrn.3312874