Türkçe Hedef-tabanlı Duygu Analizi

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2020

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Özkan, Deniz
Turhan, Çiğdem

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Software Engineering
(2005)
Department of Software Engineering was founded in 2005 as the first department in Ankara in Software Engineering. The recent developments in current technologies such as Artificial Intelligence, Machine Learning, Big Data, and Blockchains, have placed Software Engineering among the top professions of today, and the future. The academic and research activities in the department are pursued with qualified faculty at Undergraduate, Graduate and Doctorate Degree levels. Our University is one of the two universities offering a Doctorate-level program in this field. In addition to focusing on the basic phases of software (analysis, design, development, testing) and relevant methodologies in detail, our department offers education in various areas of expertise, such as Object-oriented Analysis and Design, Human-Computer Interaction, Software Quality Assurance, Software Requirement Engineering, Software Design and Architecture, Software Project Management, Software Testing and Model-Driven Software Development. The curriculum of our Department is catered to graduate individuals who are prepared to take part in any phase of software development of large-scale software in line with the requirements of the software sector. Department of Software Engineering is accredited by MÜDEK (Association for Evaluation and Accreditation of Engineering Programs) until September 30th, 2021, and has been granted the EUR-ACE label that is valid in Europe. This label provides our graduates with a vital head-start to be admitted to graduate-level programs, and into working environments in European Union countries. The Big Data and Cloud Computing Laboratory, as well as MobiLab where mobile applications are developed, SimLAB, the simulation laboratory for Medical Computing, and software education laboratories of the department are equipped with various software tools and hardware to enable our students to use state-of-the-art software technologies. Our graduates are employed in software and R&D companies (Technoparks), national/international institutions developing or utilizing software technologies (such as banks, healthcare institutions, the Information Technologies departments of private and public institutions, telecommunication companies, TÜİK, SPK, BDDK, EPDK, RK, or universities), and research institutions such TÜBİTAK.

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Abstract

Çoğu müşteri bir ürünü satın almayı düşündüklerinde, o ürünü daha önceden satın almış ve kullanmış diğer tüketicilerin inceleme ve yorumlarına güvenir. İnsanların fikir ve tercihlerini online platformlarda paylaşması yaygınlaştıkça, bu devasa bilgi kaynağı şirketlerin ürünleri hakkında geri bildirim alabilmeleri için çok değerli hale gelmiştir. Bu yüzden araştırmacılar, veri madenciliği ile duygulardan yararlı bilgileri ayrıştırmak gibi önemli bir amaç edinmişlerdir. Bu tezin hedefi, bir akıllı telefon hakkındaki Türkçe incelemelerin duygu sınıflarının belirlenmesi için doğal dil işleme kullanılarak; performans, fiyat ve kamera hedefleri bazında hedef-tabanlı duygu analizini gerçekleştirmektir. Kullanılan teknikler veri ön işlenmesi, açık ve kapalı özellik çıkarımı ve bunların ilgili hedeflere gruplanması, kelime ve kelime grupları seviyesinde sözlük tabanlı duygu analizidir. Sonuçlar, incelenen hedefler için en yüksek kesinlik, duyarlılık ve F1 ölçümü değerlerinin sırasıyla %93, %94 ve %93 olduğunu göstermiştir. Bu sonuçlar bizim çalışmamızın, diğer Türkçe hedef tabanlı duygu analizi çalışmalarıyla karşılaştırıldığında, kayda değer bir performansa sahip olduğunu ortaya çıkarıyor.
Most customers rely on reviews and comments of other consumers that already purchased and used the products that they intend to purchase. As the sharing opinions and preferences of people on online platforms are widespread, these huge data sources are highly valuable to companies to gather feedback on their products. Therefore, researchers have an essential data mining goal to extract useful information from sentiments. In this thesis, the aim is to perform an aspect-based analysis to determine the sentiment polarity of the reviews for a smart phone using natural language processing techniques in Turkish for the performance, price and camera aspects. The techniques used are data preprocessing, explicit and implicit feature extraction as well as grouping corresponding aspects and lexicon-based sentiment analysis at word-level and word-group level. The evaluations show that the highest values of precision, recall and f1 measure for the aspects examined are found to be 93%, 94% and 93%, respectively. These results reveal that our study has remarkable performance compared to other Turkish aspect-based sentiment analysis studies.

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Bilgisayar Mühendisliği Bilimleri-Bilgisayar ve Kontrol, Computer Engineering and Computer Science and Control

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78