Detecting User Emotions in Twitter through Collective Classification

İbrahim İleri, Pinar Karagoz


The explosion in the use of social networks has generated a big amount of data including user opinions about varying 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. Due to the fact that many different emotions are needed to be dealt with at this point, the problem gets more complicated as the number of emotions to be detected increases. In this study, a different user-centric approach for emotion detection is considered such that connected users may be more likely to hold similar emotions; therefore, leveraging relationship information can complement emotion inference task in social networks. Employing Twitter as a source for experimental data and working with the proposed collective classification algorithm, emotions of the users are predicted in a collaborative setting.


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Paper Citation

in Harvard Style

İleri İ. and Karagoz P. (2016). Detecting User Emotions in Twitter through Collective Classification . In Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2016) ISBN 978-989-758-203-5, pages 205-212. DOI: 10.5220/0006037502050212

in Bibtex Style

author={İbrahim İleri and Pinar Karagoz},
title={Detecting User Emotions in Twitter through Collective Classification},
booktitle={Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2016)},

in EndNote Style

JO - Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2016)
TI - Detecting User Emotions in Twitter through Collective Classification
SN - 978-989-758-203-5
AU - İleri İ.
AU - Karagoz P.
PY - 2016
SP - 205
EP - 212
DO - 10.5220/0006037502050212