Öğrenci Yayınları Bildiri Koleksiyonuhttps://hdl.handle.net/11729/52372024-03-29T01:33:58Z2024-03-29T01:33:58ZLeveraging transformer-based language models for enhanced service insight in tourismEr, AleynaÖzçelik, Şuayb Talhahttps://hdl.handle.net/11729/59042024-02-20T17:16:44Z2023-12-22T00:00:00ZLeveraging transformer-based language models for enhanced service insight in tourism
Er, Aleyna; Özçelik, Şuayb Talha
Customer feedback is a valuable resource for enhancing customer experience and identifying areas that require improvement. Utilizing user insights allows a tourism company to identify and address problematic points in its service delivery, provide feedback to partner companies regarding their product offerings, and even reconsider agreements by incorporating these opinions when curating their product portfolio. Setur implemented a systematic approach to collecting customer feedback by distributing "after-stay surveys'' to its customers via email following the completion of the agency services provided. Guest answers to open-ended questions that gather opinions about travel experience are analyzed by four tasks: user intention for answering, the sentiment of the review, subjects touched upon, and whom it concerned. For these tasks, transformer-based natural language processing (NLP) models BERT, DistilBERT, RoBERTa, and Electra are fine-Tuned to classify customer reviews. Based on the test results, it is observed that best practices could be gathered using Bert. In addition, we showed that different insights can be obtained from text comments made for two hotels in Aydin, Turkiye. Some users made complaints using neutral sentences. In some cases, people gave high scores to the numerical rating questions, but their open-ended questions could have a negative meaning.
2023-12-22T00:00:00ZForecasting and analysis of energy consumption and waste generation in Antalya with SVRÖzçelik, Şuayb TalhaTek, Faik BorayŞekerci, Erdalhttps://hdl.handle.net/11729/59032024-02-20T16:54:05Z2023-12-24T00:00:00ZForecasting and analysis of energy consumption and waste generation in Antalya with SVR
Özçelik, Şuayb Talha; Tek, Faik Boray; Şekerci, Erdal
Antalya, a rapidly expanding coastal city in Türkiye, has experienced significant changes due to urbanization and increasing tourism activities. Comprehending tourism trends is crucial for the city's sustainable development and environmental management. Based on this perspective, this paper aims to present a comprehensive retrospective analysis of Antalya's energy consumption, domestic solid waste generation, wastewater generation, population growth, and tourist numbers over the years. Antalya faces significant challenges due to escalating trends in listed areas. Utilizing the Support Vector Regression, this study projects a need for an additional 1715 GWh of electricity production capacity, an expansion of wastewater capacity by 85639 thousand m3, and an increase in domestic solid waste disposal capacity by 597745 tons by 2028 to accommodate growing demands. We emphasize the importance of adopting effective policies and strategies to support energy efficiency, waste reduction, and wastewater management alongside sustainable urban planning and tourism management for Antalya's long-Term environmental sustainability and development. The findings presented in this study provide valuable insights for policymakers, urban planners, and stakeholders to make informed decisions, ensuring a balanced approach toward economic growth and environmental conservation.
2023-12-24T00:00:00ZApplication of ChatGPT in the tourism domain: potential structures and challengesKılıçlıoğlu, Orkun MehmetÖzçelik, Şuayb TalhaYöndem, Meltem Turhanhttps://hdl.handle.net/11729/59022024-02-20T16:34:51Z2023-12-23T00:00:00ZApplication of ChatGPT in the tourism domain: potential structures and challenges
Kılıçlıoğlu, Orkun Mehmet; Özçelik, Şuayb Talha; Yöndem, Meltem Turhan
The tourism industry stands out as a sector where effective customer communication significantly influences sales and customer satisfaction. The recent shift from traditional natural language processing methodologies to state-of-The-Art deep learning and transformer-based models has revolutionized the development of Conversational AI tools. These tools can provide comprehensive information about a company's product portfolio, enhancing customer engagement and decision-making. One potential Conversational AI application can be developed with ChatGPT. In this study, we explore the potential of using ChatGPT, a cutting-edge Conversational AI, in the context of Setur's products and services, focusing on two distinct scenarios: intention recognition and response generation. We incorporate Setur-specific data, including hotel information and annual catalogs. Our research aims to present potential structures and strategies for utilizing Language Model-based systems, particularly ChatGPT, in the tourism domain. We investigate the advantages and disadvantages of three different architectures and evaluate whether a restrictive or more independent model would be suitable for our application. Despite the impressive performance of Large Language Models (LLMs) in generating human-like dialogues, their end-To-end application faces limitations, such as system prompt constraints, fine-Tuning challenges, and model unavailability. Moreover, semantic search fails to deliver satisfactory performance when searching filters that require clear answers. To address these issues, we propose a hybrid approach that employs external interventions, the assignment of different GPT agents according to intent analysis, and traditional methods at specific junctures, which will facilitate the integration of domain knowledge into these systems.
2023-12-23T00:00:00ZAssessing dyslexia with machine learning: a pilot study utilizing Google ML KitEroğlu, GünetHarb, Mhd Raja Abouhttps://hdl.handle.net/11729/58892024-01-29T06:30:34Z2023-12-19T00:00:00ZAssessing dyslexia with machine learning: a pilot study utilizing Google ML Kit
Eroğlu, Günet; Harb, Mhd Raja Abou
In this study, we explore the application of Google ML Kit, a machine learning development kit, for dyslexia detection in the Turkish language. We collected face-tracking data from two groups: 49 dyslexic children and 22 typically developing children. Using Google ML Kit and other machine learning algorithms based on eye-tracking data, we compared their performance in dyslexia detection. Our findings reveal that Google ML Kit achieved the highest accuracy among the tested methods. This study underscores the potential of machine learning-based dyslexia detection and its practicality in academic and clinical settings.
2023-12-19T00:00:00Z