Metin madenciliği ile Türkiye’nin yabancı basındaki imajının analizi

Öz

Son yıllarda, birçok gazete ve haber sağlayıcısı içeriklerini web sayfaları ve sosyal medya aracılığıyla sunmaya başlamıştır. Bu dönüşüm, haber içeriklerinin hacminde büyük bir artışa yol açarak bu geniş bilgi akışının analiz edilmesi ve yönetilmesini zorunlu hale getirmiştir. Bu çalışma kapsamında, Fox, The Guardian, BBC ve CNN gibi büyük yabancı haber sağlayıcılarının web sayfalarından Türkiye ile ilgili 8.385 haber içeriği toplanmıştır. Geleneksel teknikler, haber metinlerini içeriklerine göre sınıflandırırken, bu çalışmada içerikler önceden tanımlanmış ilgi alanlarına göre sınıflandırılarak %89,36’lık ortalama bir doğruluk oranı elde edilmiştir. Ayrıca, yabancı haber içeriklerinin yayımlanma tarihleri temelinde yapılan analizler, yayımlanma tarihleri ile sınıflandırılmış ilgi alanları arasındaki ilişkiler ortaya çıkarılmıştır. Bunun yanı sıra, BERT algoritması kullanılarak toplanan haber içeriklerinde duygu analizi gerçekleştirilmiş, içeriklerin duygu kategorileri belirlenmiş ve Türkiye’nin yabancı basındaki algısı incelenmiştir.

Anahtar Kelimeler

• duygu analizi
• BERT
• metin madenciliği
• veri madenciliği
• haber analizi
• haber sınıflandırması
• haber tarihleri ve içerik ilişkisi

Classification of news about Turkey in the foreign press through text mining

Öz

In recent years, many newspapers and news providers have begun presenting their content via web pages or through social media. This shift has led to a massive increase in the volume of news content available, necessitating the analysis and management of this vast information flow. In this study, 8,385 pieces of news content related to Turkey were collected from the web pages of major foreign news content providers, including Fox, The Guardian, BBC, and CNN. While traditional techniques classify news texts into categories based on their content, this study achieved an average accuracy rate of 89.36% by classifying the contents according to eight predefined areas of interest. Moreover, analyses were conducted based on the publication dates of all foreign news content, revealing relationships between the dates of publication and the classified areas of interest. Additionally, sentiment analysis was conducted on the collected foreign news content using the BERT algorithm, which identified the sentiment categories of the contents and examined the perception of Turkey in the foreign press.

Anahtar Kelimeler

• sentiment analysis
• BERT
• text mining
• data mining
• news analysis
• classification of news
• relationship between news dates and content

Kaynakça

1. Austin, C., and Kusumoto, F., The application of Big Data in medicine: current implications and future directions, Journal of Interventional Cardiac Electrophysiology, 47, 51-59, (2016)
2. Rana, M.I., Khalid, S., and Akbar, M.U., News classification based on their headlines: A review, 17th IEEE International Multi Topic Conference 2014, (2014)
3. Hassani, H., Beneki, C., Unger, S., Mazinani, M.T., and Yeganegi, M.R., Text mining in big data analytics, Big Data and Cognitive Computing, 4, 1, 1, (2020)
4. Kaur, G., and Bajaj, K., News classification and its techniques: a review, IOSR Journal of Computer Engineering, 18, 1, 22-26, (2016)
5. Dadgar, S.M.H., Araghi, M.S., and Farahani, M.M., A novel text mining approach based on TF-IDF and Support Vector Machine for news classification, 2016 IEEE International Conference on Engineering and Technology (ICETECH), (2016)
6. Ghomi, H., and Hussein, M., An integrated text mining, literature review, and meta-analysis approach to investigate pedestrian violation behaviours, Accident Analysis & Prevention, 173, 106712, (2022)
7. Gomes, H., de Castro Neto, M., and Henriques, R., Text Mining: Sentiment analysis on news classification, 2013 8th Iberian Conference on Information Systems and Technologies (CISTI), (2013)
8. Zhang, X., and Li, W., From social media with news: Journalists’ social media use for sourcing and verification, Journalism Practice, 14, 10, 1193-1210, (2020)

9. Cetina Presuel, R., and Sierra, J.M.M., Algorithms and the news: social media platforms as news publishers and distributors, Revista De Comunicación, 18, 2, 261-285, (2019)
10. Carreira, R., Crato, J.M., Gonçalves, D., and Jorge, J.A., Evaluating adaptive user profiles for news classification, Proceedings of the 9th international conference on Intelligent user interfaces, (2004)
11. Leonard, G., Sisnadi, F., Wardhana, N.V., Al-Ghofari, M.A.A., and Girsang, A.S., News Classification Based On News Headline Using SVC Classifier, 2022 16th International Conference on Telecommunication Systems, Services, and Applications (TSSA), (2022)
12. Agarwal, J., Christa, S., Pai, A., Kumar, M.A., and Prasad, G., Machine learning application for news text classification, 2023 13th International Conference on Cloud Computing, Data Science & Engineering (Confluence), (2023)
13. Sadjadi, S., Mashayekhi, H., and Hassanpour, H., A two-level semi-supervised clustering technique for news articles, International Journal of Engineering, 34, 12, 2648-2657, (2021)
14. Miao, F., Zhang, P., Jin, L., and Wu, H., Chinese news text classification based on machine learning algorithm, 2018 10th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), (2018)
15. Salehin, K., Alam, M.K., Nabi, M.A., Ahmed, F., and Ashraf, F.B., A comparative study of different text classification approaches for bangla news classification, 2021 24th International Conference on Computer and Information Technology (ICCIT), (2021)
16. Lin, S.-Y., Kung, Y.-C., and Leu, F.-Y., Predictive intelligence in harmful news identification by BERT-based ensemble learning model with text sentiment analysis, Information Processing & Management, 59, 2, 102872, (2022)
17. Hayawi, K., Shahriar, S., Serhani, M.A., Taleb, I., and Mathew, S.S., ANTi-Vax: a novel Twitter dataset for COVID-19 vaccine misinformation detection, Public health, 203, 23-30, (2022)
18. To, Q.G., To, K.G., Huynh, V.-A.N., Nguyen, N.T., Ngo, D.T., Alley, S.J., Tran, A.N., Tran, A.N., Pham, N.T., and Bui, T.X., Applying machine learning to identify anti-vaccination tweets during the COVID-19 pandemic, International journal of environmental research and public health, 18, 8, 4069, (2021)
19. Ahmed, J., and Ahmed, M., Online news classification using machine learning techniques, IIUM Engineering Journal, 22, 2, 210-225, (2021)
20. MAHAJAN, S., and Ingle, D., News classification using machine learning, Int. J. Recent Innov. Trends Comput. Commun, 9, 5, 23-27, (2021)
21. Sunagar, P., Kanavalli, A., Nayak, S.S., Mahan, S.R., Prasad, S., and Prasad, S., News Topic Classification Using Machine Learning Techniques, International Conference on Communication, Computing and Electronics Systems: Proceedings of ICCCES 2020, (2021)
22. Fanny, F., Muliono, Y., and Tanzil, F., A comparison of text classification methods k-NN, Naïve Bayes, and support vector machine for news classification, Jurnal Informatika: Jurnal Pengembangan IT, 3, 2, 157-160, (2018)
23. Nwet, K.T., and Darren, S., Machine learning algorithms for Myanmar news classification, International Journal on Natural Language Computing (IJNLC), 8, 4, (2019)
24. Keya, A.J., Wadud, M.A.H., Mridha, M., Alatiyyah, M., and Hamid, M.A., AugFake-BERT: handling imbalance through augmentation of fake news using BERT to enhance the performance of fake news classification, Applied Sciences, 12, 17, 8398, (2022)
25. Shishah, W., Fake news detection using BERT model with joint learning, Arabian Journal for Science and Engineering, 46, 9, 9115-9127, (2021)
26. Mehta, D., Dwivedi, A., Patra, A., and Anand Kumar, M., A transformer-based architecture for fake news classification, Social network analysis and mining, 11, 1-12, (2021)
27. Sparck Jones, K., A statistical interpretation of term specificity and its application in retrieval, Journal of documentation, 28, 1, 11-21, (1972)
28. Takenobu, T., Text categorization based on weighted inverse document frequency, Information Processing Society of Japan, SIGNL, 94, 100, 33-40, (1994)
29. Sabbah, T., Selamat, A., Selamat, M.H., Al-Anzi, F.S., Viedma, E.H., Krejcar, O., and Fujita, H., Modified frequency-based term weighting schemes for text classification, Applied Soft Computing, 58, 193-206, (2017)
30. Breiman, L., Random forests, Machine learning, 45, 5-32, (2001)
31. Boser, B.E., Guyon, I.M., and Vapnik, V.N., A training algorithm for optimal margin classifiers, Proceedings of the fifth annual workshop on Computational learning theory, (1992)
32. Fix, E., and Hodges, J.L., Discriminatory analysis, nonparametric discrimination, (1951)
33. McCallum, A., and Nigam, K., A comparison of event models for naive bayes text classification, AAAI-98 workshop on learning for text categorization, (1998)
34. Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K., Bert: Pre-training of deep bidirectional transformers for language understanding, arXiv preprint arXiv:1810.04805, (2018)
35. ISIK, M., and AYDEMİR, E., News about Turkey from BBC, CNN, TG, and FOX. (2024)
36. Fawcett, T., An introduction to ROC analysis, Pattern recognition letters, 27, 8, 861-874, (2006)
37. Powers, D.M., Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation, arXiv preprint arXiv:2010.16061, (2020)
38. Takahashi, K., Yamamoto, K., Kuchiba, A., and Koyama, T., Confidence interval for micro-averaged F 1 and macro-averaged F 1 scores, Applied Intelligence, 52, 5, 4961-4972, (2022)