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Journals / Bilecik Şeyh Edebali Üniversitesi Sağlık Bilimleri Fakültesi Dergisi / 2026 / Volume: 4 Issue: 3

Sentiment Analysis of Telehealth News Using the Orange Data Mining Program

Orange Veri Madenciliği Programı Kullanılarak Tele Sağlık Haberlerinin Duygu Analizi

Gözde Koca(BİLECİK ŞEYH EDEBALİ ÜNİVERSİTESİ, İKTİSADİ VE İDARİ BİLİMLER FAKÜLTESİ)0000-0001-6847-6812
Pages
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Publication Date
2026-09-11
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The results indicate that positive sentiment was more prevalent in telehealth-related news, with particular emphasis on the advantages of digitalization and the facilitation of access to healthcare services.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Telehealth has provided a significant transformation in the health system, especially since the emergence of the COVID-19 pandemic, and it has contributed to the widespread adoption of digital health services.
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Key Findings & Data:In this study, sentiment analysis was conducted on news articles about telehealth published in The New York Times between January 1, 2020, and March 8, 2025.
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Conclusion & Impact:The results indicate that positive sentiment was more prevalent in telehealth-related news, with particular emphasis on the advantages of digitalization and the facilitation of access to healthcare services.
🔬Design:Research Article
📍Scope:2020 Verileri
⚙️Variables:Telehealth · Sentiment Analysis · Digital Health

Abstract

Telehealth has provided a significant transformation in the health system, especially since the emergence of the COVID-19 pandemic, and it has contributed to the widespread adoption of digital health services. Recently, it has become more widely discussed in the public due to its facilitation of access to healthcare services and the acceleration of patient care processes. Social media and news platforms have extensively covered these discussions. In this study, sentiment analysis was conducted on news articles about telehealth published in The New York Times between January 1, 2020, and March 8, 2025. A total of 295 news articles were analyzed. During the analysis process, the emotional distribution of the news was determined, and the way the concept of telehealth was addressed in social, economic, political, and technological contexts was evaluated. The Orange Data Mining program was used in the study, and the news was analyzed to determine whether it contained positive, negative, or neutral sentiment. The results indicate that positive sentiment was more prevalent in telehealth-related news, with particular emphasis on the advantages of digitalization and the facilitation of access to healthcare services.

Özet

Tele Sağlık, özellikle COVID-19 pandemisinin ortaya çıkışından bu yana sağlık sisteminde önemli bir dönüşüm sağlamış ve dijital sağlık hizmetlerinin yaygınlaşmasına katkıda bulunmuştur. Son yıllarda sağlık hizmetlerine erişimi kolaylaştırması ve hasta bakım süreçlerini hızlandırması nedeniyle kamuoyunda daha fazla tartışılmaya başlanmıştır. Bu tartışmalar, özellikle sosyal medya ve haber platformlarında geniş yer bulmaktadır. Bu çalışmada, The New York Times gazetesinde 1 Ocak 2020 ile 8 Mart 2025 tarihleri arasında Tele Sağlık ile ilgili yayınlanan haberlerin duygu analizi incelenmiştir. Toplam 295 haber analiz edilmiştir. Analiz sürecinde, haberlerin duygusal dağılımı belirlenmiş ve Tele Sağlık kavramının toplumsal, ekonomik, siyasi ve teknolojik bağlamda nasıl ele alındığı değerlendirilmiştir. Çalışmada Orange Data Mining programı kullanılmış ve haberlerin olumlu, olumsuz veya nötr içerikte olup olmadığı analiz edilmiştir. Elde edilen sonuçlar, Tele Sağlık ile ilgili haberlerde olumlu duygunun daha fazla bulunduğunu, özellikle sağlık hizmetlerine erişimin kolaylaşmasının ve dijitalleşmenin avantajlarının vurgulandığını göstermektedir.

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Cite This Article

Koca, G. (2026). Sentiment Analysis of Telehealth News Using the Orange Data Mining Program. Bilecik Şeyh Edebali Üniversitesi Sağlık Bilimleri Fakültesi Dergisi, Volume: 4 Issue: 3. https://doi.org/10.61535/bseusbfd.1663490

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