Yağcı, Semih

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https://hdl.handle.net/20.500.14365/7591
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semih.yagci@ieu.edu.tr
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05.04. Software Engineering
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0009-0002-7754-4148
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59491387500
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42A8201965F569FD
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    Aspect-Based Sentiment Annotation and Analysis System
    (Springer Science and Business Media Deutschland GmbH, 2025) Terziler, D.E.; Çekirdek, H.; Yağcı, S.; Kumova Metin, S.
    Today, the ever-increasing amount of user-generated content and online activities has increased the transfer of human opinions and feelings through unstructured text (chat, blogs, reviews, etc.) to high levels. This has made emotion/sentiment recognition from unstructured text one of the most popular tasks in the natural language processing field. In current studies, not only the feeling that is conveyed by the whole text is to be recognized but also the sentiment/feeling that is directed to a certain aspect is to be analyzed. In this study, we have built an aspect-based sentiment annotation and analysis system. The system provides an intelligent and user-friendly interface to construct an annotated dataset by highlighting aspect candidates and selecting the sentiment label value for a given aspect. It also gives recommendations to its users with possible aspect candidates and their corresponding sentiment values. The recommendation service in our system is based on existing AI models that have been used for aspect-based sentiment analysis. © 2025 Elsevier B.V., All rights reserved.
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    Analyzing Traffic Patterns in Izmir: a Study on Busy Hours and Congestion
    (Institute of Electrical and Electronics Engineers Inc., 2024) Başoğul, Ali Ozan; Çekirdek, Hamza; Nakipoğlu, K.; Yağcı, Semih; Demir, Alper
    Developing strategies for transportation is one of the main tasks for smart cities. With the traffic data on key arteries of Izmir, this project aims to estimate the time intervals and routes where traffic jam may occur in Izmir and uncover relationships between various factors affecting traffic. The study demonstrates that accurate models can be developed to predict the number of passing vehicles and reveals interesting correlations within the data. © 2024 IEEE.