The aim of this study is to address the issues identified in previous reviews and meta-analyses regarding the progress of patient experience literature and to highlight the most important concepts specifically related to digital health technologies. To do so, we have carried out a comprehensive analysis of the literature on patient experience in the category of health science services databases over the past decade and identified the tools related to digital health technologies within these studies. This is a bibliometric study based on data obtained from the Web of Science and Scopus between the years 2014 to and 2024 by using 11 search terms. In this review, a total of 21,392 publications from patient experience literature over the last decade were analyzed. Keywords were grouped by showing their co-occurrence using bibliometric and scientific mapping analyzing methods. The development of digitalization and digital tools has contributed to the advancement of theory in the field of digital health, eHealth, electronic health records, health information technology, the internet, mhealth, mobile applications, mobile health, patient portals, smartphones, social media, telemonitoring, web, artificial intelligence, machine learning, virtual reality, telehealth, telemedicine, telerehabilitation, and virtual care. These developments have provided sustainable digital health benefits in the development of patient experience theories. The findings of this study emphasize that digital health tools cover a wide area of research, and the application of information and communication technologies goes beyond the field of medicine and covers the broad field of healthcare.

Reputation is shaped depending on factors such as the quality of products and services offered by a brand to its stakeholders, its reliability, and its innovative aspect in the eyes of stakeholders. The sustainability of a brand reputation depends on the brand creating a positive perception by fulfilling its social responsibilities and maintaining this perception in the long term. In this study, the brand reputation of Hewlett-Packard (HP) computers is evaluated through customer reviews. The data set in the study consists of 2012 customer reviews obtained from Hepsiburada, one of the most widely used e-commerce platforms in Turkey. Sentiment analysis and text mining artificial intelligence methods were used in the study. For sentiment analysis, the Naive Bayes method, which is one of the machine learning methods, was used, and the comments were divided into three groups as positive, negative, and neutral. In the study, 82% of the customer comments were positive, 11% were negative, and 7% were neutral. The fact that most of the comments consist of positive sentiments shows that HP Computer has a positive reputation in the eyes of stakeholders consisting of customers. Comments consisting of negative and neutral emotions show the aspects that the brand needs to improve. In the study, the text mining method emphasises the prominent features of the brand in the comments. This study makes an important contribution to the reputation assessment of brands and to ensuring sustainable brand reputation.

In parallel with the advances in technology, digital journalism is preferred more than printed journalism day by day. Due to the fast and up-to-date sense of journalism provided by digital journalism and its ubiquitous accessibility features, it is read more by users. In addition to these advantages provided by digital journalism, it also has some difficulties compared to printed journalism. The stage of preparation and delivery of the news to the user requires more technological knowledge and equipment compared to printed journalism. The processes of title selection, text creation, photo selection and determination of the appropriate news category in the preparation phase of the news are designed to be both faster and user-friendly compared to printed publishing. The news created to be presented to the target audience may belong to one or more of different categories such as economy, politics, sports, technology, and health. The inclusion of the news in the appropriate category provides convenience in terms of reaching the right audience and archiving the news correctly. In this study, news texts were classified according to their categories based on the machine learning methods. In the study, news of five newspapers in three different categories were used. Bayesian classifier and decision tree methods were used to classify the news in the dataset including a total of 10.500 news. In the results of the study, it was observed that the Bayesian classifier classified the news more successfully according to their categories.

Brand reputation directly influences customer trust and decision-making. A good reputation can lead to greater customer loyalty, commitment, and advocacy. This study aims to understand the effects of brand reputation on customer trust and loyalty and to determine how brands can protect their reputation. This study, which was conducted on the iPhone 11 sample by obtaining statistical data from customer reviews, can be adapted and used by researchers and companies that want to measure brand reputation. In this study, customer reviews for the iPhone 11 phone on the Trendyol e-commerce site, the largest e-commerce platform in Turkey, are analyzed using sentiment analysis and machine learning methods. While 85 percent of customers are satisfied with the iPhone 11, 13 percent are dissatisfied with it. The neutral comment rate of 2 percent indicates that some customers do not express a clear positive or negative opinion about the product. In the comments of customers who bought the iPhone 11, there are those who express satisfaction with the quality, technical features, performance, and price/performance ratio of the product, as well as those who express significant complaints about delivery, quality, price, and customer service. Neutral comments generally focus on the product itself, price, quality, shipping, and packaging, and make informative evaluations. A sustainable reputation is based on the extent to which an organization embraces ethical principles, social responsibility, and sustainable practices throughout its operations and business relationships. Brands can improve, protect, and increase their brand reputation by considering and analyzing the thoughts and feelings of their customers. For this, they should develop policies and strategies to reinforce their strong features and improve their faulty and deficient features.

Gelişen teknolojik olanaklara bağlı olarak iletişim alanında da önemli değişiklikler yaşanmıştır. İnternetin yaygınlaşması ile geleneksel iletişim araçlarının yerini bilgiye ulaşmanın hızlı ve kolay olduğu yeni teknolojik yöntemler almıştır. Bu teknolojik yeniliklerin başında da kullanıcıların interaktif iletişimine olanak sağlayan sosyal medya platformları gelmektedir. Kullanıcıların hizmetine sunulan birçok sosyal ağ arasında Twitter, yazılı ve görsel habercilik için uygun bir platform olması nedeniyle hem gündemi takip etmek isteyen kullanıcılar hem de haberini hızla hedef kitleye ulaştırmak isteyen haber kaynakları tarafından yoğun olarak tercih edilmektedir. Haberin insanlar arasında hızla yayılması ve etkileşim sağlamasına olanak sunan bu platformun avantajları yanında bazı dezavantajları da bulunmaktadır. Haberin kontrol edilememesi nedeniyle sahte haberlerin dolaşıma sokulması ve bunların engellenme güçlüğü bunlardan bazılarıdır. Bu çalışmada Twitter’da sahte haberleri tespit etmek için makine öğrenmesi yöntemleri kullanılmıştır. Örnek bir konu seçilmiş ve bununla ilgili yapılmış sahte ve gerçek haberler tespit edilmiştir. Çalışmada karar ağaçları ve Naive Bayes yöntemleri kullanılmıştır. Çalışmanın sonuçları karışıklık matrisi ve F1 skoru yöntemine göre karşılaştırılmıştır. Karar ağaçları yönteminin F1 skoru 0,829, Naive Bayes yönteminin ise 0,883 olmuştur. Bu sonuçlara göre Naive Bayes yönteminin Twitter’da sahte haber tespiti için daha başarılı bir yöntem olduğu görülmüştür. Bu çalışma ile Twitter’da sahte haberlerin tespiti yapılabilecek ve önlemler alınabilecektir.

This study aims to analyse consumer experiences, purchase behaviours, and emotional responses through Booking and Expedia’s mobile applications. The 2000 user reviews collected from Google Play were subjected to a comprehensive sentiment analysis, text mining, and topic modelling process to identify the key elements that shape consumers’ emotional experiences and purchase decisions. According to the results of text mining and sentiment analysis performed with Python’s WordNet library, 81.9% of Booking.com reviews are positive, 8.4% are negative, and 11.3% are neutral, whereas 55.8% of Expedia reviews are positive, 37.8% are negative, and 8.0% are neutral. In the topic modelling analysis, Booking.com emphasised ease of booking, while Expedia emphasised difficulties in cancellation and refund processes. These findings provide valuable insights into how consumers’ emotional states and purchasing behaviours are reflected in their experiences with mobile applications. The study enables the development of strategic recommendations for marketing management to better analyse consumers’ expectations and experiences.