Turkey has received consistent criticism from international media for having many naturalized athletes in its national squad, both in the Olympic Games and other major international sporting events. Similar criticisms have also been a feature of debates for a long time in domestic media, varying in views toward these athletes. This research focuses on media representations of naturalized athletes in Turkey between 2008 and 2020. We investigated the sentiments of news items from four major Turkish newspapers (Milliyet, Cumhuriyet, Sabah and Fanatik) on their stances toward naturalized athletes over the timespan of 2008–2020. Beside analyzing the sentiment of the media content both cumulatively and fragmentedly, we also identified the yearly trends and most featured sports in this context, combining qualitative and quantitative techniques. Our findings showed that sentiments in Turkish media toward naturalized athletes are mostly neutral and negative as well as with differences varying on the basis of the newspapers and news item types. The most criticism underlined pursuing “shortcut” success with naturalized athletes representing Turkey in the international arena. Among the featured sports, basketball, football, and track and field have been the most discussed ones in the naturalization context.
This paper presents a Decision Support System (DSS) that helps companies with corporate reputation (CR) estimates of their respective brands by collecting provided feedbacks on their products and services and deriving state-of-the-art key performance indicators. A Sentiment Analysis Engine (SAE) is at the core of the proposed DSS that enables to monitor, estimate, and classify clients’ sentiments in terms of polarity, as expressed in public comments on social media (SM) company channels. The SAE is built on machine learning (ML) text classification models that are cross-source trained and validated with real data streams from a platform like Trustpilot that specializes in user reviews and tested on unseen comments gathered from a collection of public company pages and channels on a social networking platform like Facebook. Such crosssource opinion analysis remains a challenge and is highly relevant in the disciplines of research and engineering in which a sentiment classifier for an unlabeled destination domain is assisted by a tagged source task (Singh and Jaiswal, 2022). The best performance in terms of F1 score was obtained with a multinomial naive Bayes model: 0,87 for validation and 0,74 for testing.
