Tezin Türü: Yüksek Lisans
Tezin Yürütüldüğü Kurum: Orta Doğu Teknik Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü, Türkiye
Tezin Onay Tarihi: 2018
Öğrenci: FATİH KURT
Danışman: PINAR KARAGÖZ
Özet:This work investigates segmentation approaches for informal short texts in morphologically rich languages in order to e ectively classify the sentiment. The two building blocks of the proposed work in this thesis are segmentation and deep neural network model building. Segmentation focuses on preprocessing of text with di erent methodologies. These methodologies are grouped under four distinct approaches; namely, morphological, sub-word, tokenization, and hybrid approaches. There is mostly multiple numbers of variants for each of these four methods provided in this work. The second stage focuses on e ective model building for classifying text. Performances of each method are evaluated by utilizing a model built by a Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) model proposed in the literature for text classi cation.