Nükleer enerjiye geçişte Türk toplumunun tepkisinin duygu analizi ile tespit edilmesi
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PDF downloads: 12
Keywords:
NLP, Transformer, Duygu Analizi, Çoklu Etiket, Nükleer EnerjiAbstract
Nükleer enerjiye geçişte toplum tarafından verilen tepkiler sosyal medyada yer alan yorumların
analizi ile ölçülebilmektedir. Ancak, binlerce yorumun tek tek okunarak incelenmesi her zaman mümkün
değildir. Bu zorlukla baş edebilmek için, Transformer yöntemi duygu analizi maksadıyla çalışmamızda
kullanılmıştır. Bu yaklaşım, diğer derin öğrenme yöntemlerine kıyasla başarılı sonuçların daha kısa süre
içinde elde edilmesi nedeniyle daha verimli bulunmaktadır. Bu çalışmada, özellikle "kirli metin" olarak
adlandırılan sosyal medya yorumlarının analizi üzerine odaklanan çoklu-etiketli doğal dil işleme (NLP)
modeli tanımlanmıştır. Türkçe yorumlarda, nükleer enerji santrallerinin kurulmasıyla ilgili pozitif
duyguların yanı sıra yaygın olarak olumsuz bakış açıları da bulunmaktadır. Bu çalışmanın temel amacı,
Türk toplumunun zaman içinde nükleer enerjiye geçişe yönelik dinamik algısını belirlemek ve metin
madenciliğinde öne çıkan bir teknik olan NLP'yi kullanarak kapsamlı bir analiz yapmaktır. Çalışmadan
elde edilen sonuçların, karar vericilerin geçişle ilgili politikalar oluşturmasına rehberlik etmesi
amaçlanmaktadır.
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References
Jungherr, A., Jürgens, P., & Schoen, H. (2012). Why the pirate party won the German election of 2009 or the trouble with predictions: A response to Tumasjan, A., Sprenger, T. O., Sander, P. G., & Welpe, I. M. "Predicting elections with Twitter: What 140 characters reveal about political sentiment." Social Science Computer Review, 30(2), 229-234. https://doi.org/10.1177/0894439311404119.
Garcia, M.N.M. (2020). Information retrieval and social media mining. Information, 1(12), 578; https://doi.org/10.3390/info11120578.
Babu, M.S., Ali, A.A., Rao, A.S. (2014). A Study on Information Retrieval Methods in Text Mining. International Journal of Engineering Research & Technology (IJERT) NCDMA – 2014, 2(15). https://doi.org/10.17577/IJERTCONV2IS15028
Aftab, F., Bazai, S.U., Marjan, S., Baloch, L., Aslam, S., Amphawan, A., Neo, T.-K., 2023. A comprehensive survey on sentiment analysis techniques. International Journal of Technology, 14(6), 1288-1298. https://doi.org/10.14716/ijtech.v14i6.6632
Kokab, S.T., Asghar, S., & Naz, S. (2022). Transformer-based deep learning models for the sentiment analysis of social media data. Array, 14, 100157. https://doi.org/10.1016/j.array.2022.100157.
Pejić Bach, Mirjana; Krstić, Živko; Seljan, Sanja; Turulja, Lejla (2019). Text mining for big data analysis in financial sector: A literature review. Sustainability, 11(5), 1277. https://doi.org/10.3390/su11051277.
Sun, S., Luo, C., & Chen, J. (2017). A review of natural language processing techniques for opinion mining systems. Information Fusion, 36, 10–25. https://doi.org/10.1016/j.inffus.2016.10.004.
Daniel W.O., Julian R.M., and Jugal K.K. (2020). A survey of the usages of deep learning for natural language processing. IEEE Transactions on Neural Networks and Learning Systems, 99, 1-21. https://doi.org/10.1109/TNNLS.2020.2979670.
Khurana, D., Koli, A., Khatter, K., Singh, S. (2023). Natural language processing: State of the art, current trends and challenges. Multimedia Tools and Applications, 82, 3713–3744. https://doi.org/10.1007/s11042-022-13428-4.
Greff K, Srivastava RK, Koutník J, Steunebrink BR, Schmidhuber J (2016). LSTM: A search space odyssey. IEEE Transactions on Neural Networks and Learning Systems, 28(10), 2222–2232. https://doi.org/10.1109/tnnls.2016.2582924.
Mathew, L., Bindu, V.R. (2020). A review of natural language processing techniques for sentiment analysis using pre-trained models. ICCMC 2020, 340-345. https://doi.org/10.1109/ICCMC48092.2020.ICCMC-00064.
Bahdanau D, Cho K, Bengio Y (2015). Neural machine translation by jointly learning to align and translate. In ICLR 2015. https://doi.org/10.48550/arXiv.1409.0473.
Devlin J, Chang MW, Lee K, Toutanova K, (2019). Bert: pre-training of deep bidirectional transformers for language understanding. In NAACL 2019. https://doi.org/10.18653/V1/N19-1423.
Murphy, J., Link, M. W., Childs, J. H., Tesfaye, C. L., Dean, E., Stern, M., Pasek, J., Cohen, C., Callegaro, M., Harwood, P. (2014). Social media in public opinion research: Executive summary of the aapor task force on emerging technologies in public opinion research. Public Opinion Quarterly, 78(4), 788–794. http://www.jstor.org/stable/24545970.
Wu, Z., He, Q., Li, J., Bi, G., Antwi-Afari, M.F. (2023). Public attitudes and sentiments towards new energy vehicles in China: A text mining approach. Renewable and Sustainable Energy Reviews, 178, 113242. https://doi.org/10.1016/j.rser.2023.113242.
Qazi, A., Hussain, F., Rahim, N. A., Hardaker, G., Alghazzawi, D., Shaban, K., & Haruna, K. (2019). Towards sustainable energy: A systematic review of renewable energy sources, technologies, and public opinions. IEEE Access, 7, 63837–63851. https://doi.org/10.1109/ACCESS.2019.2906402.
Piselli, C., Fronzetti Colladon, A., Segneri, L., & Pisello, A. L. (2022). Evaluating and improving social awareness of energy communities through semantic network analysis of online news. Renewable and Sustainable Energy Reviews, 167, 112792. https://doi.org/10.1016/j.rser.2022.112792.
Alafwan, B., Siallagan, M., Putro, U.S. (2023). Comments analysis on social media: A review. EAI Endorsed Transactions on Scalable Information Systems 2023, 10(6). https://doi.org/10.4108/eetsis.3843.
Moy P, Tewksbury D, Rinke EM. Agenda‐setting, priming, and framing. In The International Encyclopedia of Communication Theory and Philosophy 1st ed.; Publisher: Wiley, 1-13.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I. (2017). Attention is All You Need. 31st Conference on Neural Information Processing Systems (NIPS 2017). 5998–6008. https://doi.org/10.48550/arXiv.1706.03762.
Wang, G., Smetannikov, I., & Man, T. (2020). Survey on automatic text summarization and transformer models applicability. In CCRIS: International Conference on Control, Robotics and Intelligent System, Xiamen, China, October 27-29, 176–184. https://doi.org/10.1145/3437802.3437832
Acheampong, F. A., Nunoo-Mensah, H., & Chen, W. (2021). Transformer models for text-based emotion detection: a review of bert-based approaches. Artificial Intelligence Review, 54, 5789–5829. https://doi.org/10.1007/s10462-021-09958-2.
Gruetzemacher, R., & Paradice, D. B. (2022). Deep transfer learning & beyond: Transformer language models in information systems research. ACM Computing Surveys, 54, 204:1–204:35. https://doi.org/10.1145/3505245.
