Collective classification of user emotions in twitter

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Tezin Türü: Yüksek Lisans

Tezin Yürütüldüğü Kurum: Orta Doğu Teknik Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü, Türkiye

Tezin Onay Tarihi: 2015

Öğrenci: İBRAHİM İLERİ

Danışman: PINAR KARAGÖZ

Özet:

The recent explosion of social networks has generated a big amount of data including user opinions about varied subjects. For classifying the sentiment of user postings, many text-based techniques have been proposed in the literature. As a continuation of sentiment analysis, there are also studies on the emotion analysis. Because of the fact that many different emotions are needed to be dealt with at this point, the problem becomes much more complicated. In this thesis, a different user-centric approach is considered that connected users may be more likely to hold similar emotions; therefore, leveraging relationship information can complement user-level sentiment inference task in social networks. Employing Twitter as a source for experimental data and working with a proposed collective classification algorithm, users whose emotions are not known on subject, are predicted in an effective and collaborative setting.