Kemal Kantarci

Hotel stars are key indicators for assessing locations, facilities, and other attributes. Although the World Tourism Organization has long advocated for such systems, no consensus has been reached on standardizing them. Italy and Spain use their own systems. Kazakhstan has no system. Since visitors rate hotels, Web 2.0 implementations have started using their rating systems. Hotels in countries without a standard system would be negatively affected and would be vulnerable to manipulation by Web 2.0. Topic modeling and sentiment analysis are implemented to explore how hotel stars converge or diverge regarding hotel attributes mentioned by visitors. TripAdvisor’s 5,894 online reviews of Astana’s hotels in Kazakhstan between 2006 and 2023 are used. The study pinpoints the same attributes as remaining issues, even though hotels differ in stars. Customers’ sentiments about derived hotel attributes mostly converge for hotels rated 2, 3, and 4 stars; however, they diverge in 5-star hotels. Specifically, even though hotels with 2, 3, or 4 stars differ, they share similar issues. Thus, official rating systems should be adopted in Kazakhstan soon. Kazakhstan’s hotels can directly adopt an official rating system to enhance attribute qualities.

Sentiment classification has become one of the most widely studied areas in text classification, especially in recent years. This study presents extensive experiments in sentiment analysis, investigating the performance of seven state-of-the-art sentiment analyzers (TextBlob, VADER, AFINN, Stanza, Nlptown, Sentistrength, and Flair) in Stage 1, and an ensemble approach in Stage 2, using multilingual restaurant reviews from Almaty, Kazakhstan. The reviews, either originally written in English or translated from Russian, are analyzed across various sections, including HEAD, TEXT, and their combinations (HEAD+TEXT). The results of Stage 2 ensemble methods demonstrate clear advantages of carefully selected ensembles over individual sentiment analyzers. Specifically, the highest Micro-F1 score for English reviews was 0.733 in the TEXT section, achieved by the ensemble TextBlob+Stanza+Nlptown+Sentistrength. The highest Macro-F1 score for English reviews was 0.684, achieved by the same ensemble in the TEXT section. For Russian reviews, the highest Micro-F1 score was 0.703 in the HEAD+TEXT combination, and the highest Macro-F1 score was 0.642 in the TEXT section, both achieved by the ensemble TextBlob+Stanza+Nlptown+Sentistrength. These findings highlight that the performance of sentiment analyzers varies depending on the original language and the corresponding review section.

Attractions in the tourism industry are one of the components that motivate tourists to visit destinations, such as entertainment, natural, cultural, and historical richness. For such reasons, people decide to visit unique destinations and spend time there. Almaty, the largest city of Kazakhstan, is one of the significant attraction centers of the Central Asia region, offering tourists unique and pleasant features with several tourist attractions. This study aims to analyze online user reviews of tourist attractions in Almaty, Kazakhstan, using machine learning and text mining methods. The primary focus is on identifying the main thematic clusters of reviews and their sentiment and comparing these themes with the types of attractions: historical, natural, and man-made. A total of 7,515 reviews were collected from the TripAdvisor website. The data was processed using sentiment analysis, topic modeling, and hierarchical clustering methods. The analysis revealed that 38% of the reviews were related to natural attractions, 34% to man-made, and 28% to historical ones. The most positive reviews were associated with natural attractions, while historical and man-made attractions received 79.38% and 81.40% positive reviews, respectively. In addition, the items that make up these attractions are identified, and their sentiment levels are pointed out. In addition to this situation, visitors have the most positive expressions for natural attractions, especially landscapes and lakes. The findings emphasize the importance of considering review themes to improve the quality of tourist services and to enhance the positive image of Almaty as a tourist destination.

Orhon yazıtlarından biri olan Bilge Kağan yazıtında “Türk halkı (bu huyundan) vazgeç ve nadim ol! Kutsal Ötüken Dağları Halkı, (sen kendin yerini yurdunu bırakıp) gittin” ifadesi yer almaktadır. Bilge Kağan tarafından Ötüken ormanında kal, oradan ayrılma anlamında kullanılan bu ifade bir anlamda bütün dünyadaki ve özellikle Türkiye’deki Türklerin ata yurtlarını ziyaret etme ve kökleri ile ilgili mirası görme amacıyla yapacakları seyahatlerin temel motivasyonu ve sloganı olabilir. Günümüzde bireylerin, atalarını ve köklerini araştırma, kimliğini bulma, güçlendirme ve geçmiş zamanı yaşama isteği önemli bir turizm alanı haline gelmiştir. Bu çalışmanın öncelikli amacını miras ve nostalji odaklı ortaya çıkan bu yeni turizm faaliyetleriyle ilgili uluslararası alanyazında kullanılan kavramların Türkçe karşılıklarının geliştirilmesidir. Ayrıca, bu kapsamda Türkiye ile Orta Asya arasında ortak kültürel mirasa dayalı olarak geliştirilebilecek turizm arz ve talep koşullarının planlanmasına katkı sunulması çalışmanın bir diğer amacını oluşturmaktadır. Alanyazına ve ikincil verilere dayalı olarak yapılan bu çalışma, miras odaklı gelişen turizm türlerinin Türkler ve Türkiye ve Orta Asya Ülkeleri için önemli bir fırsat olduğunu ortaya koymaktadır.

Slovakia is positioned in a highly competitive market of Central European countries with geographical and cultural similarities. Despite having a good potential, the tourism development in the country has not reached the level of the neighboring countries. In information sensitive sector such as tourism, the development is highly dependent on following and implementing new trends including latest technology. The usage of social media and Travel 2.0 platforms has become crucial in decision-making about the place to go or stay and online reviews have turned into a valuable source of information about the supplier’s performance and service quality. This study analyses hotel performance through hotel performance attributes using consumer generated reviews posted on the number one hotel rating social platform TripAdvisor. Hotel reviews based on real travel experience is a valuable source of information about the hotel’s performance evaluated by tourists directly. Several interesting findings resulted from the research and further implications were advised.

Central Asia (CA) region consist of five countries, Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan and Uzbekistan, which is the core of Asian continent. Ancient Silk Road has played the critical role of connecting the East and West through the history and today. CA has a rich history, culture and nature conditions as pull factors for world tourism market. This region as a new destination is becoming more important in the international travel and tourism market. All five countries have been experiencing transition period and have been facing some domestic and international issues with respect to tourism perspective. In this study, Turkish travelers for different motivations ranging from business to vacation visiting Central Asia are asked to evaluate the region based on some personal and expectation attributes. The investigation includes some fundamental factors such as key desires to travel to Central Asia or some mind-set not visiting there. For this purpose, approximately 200 responses are gathered from persons traveling there for different motivations including business, travel and so on. Multi Dimensional Scaling is run in order to display graphically the attributes on two dimensional graphs. This research is a pioneer work that sheds light on the future studies that will be conducted by both academicians and practitioners. Also, it reflects the Turkish travelers’ expectations to CA region as a tourism destination. The main findings can be summarized as the key factors leading people either to travel CA or not to travel there by gender, country and some other personal attributes.