Authors:
İbrahim İleri
and
Pinar Karagoz
Affiliation:
Middle East Technical University, Turkey
Keyword(s):
Social Networks, Emotion Analysis, Sentiment Analysis, Collective Classification.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Clustering and Classification Methods
;
Computational Intelligence
;
Evolutionary Computing
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Mining Text and Semi-Structured Data
;
Soft Computing
;
Symbolic Systems
Abstract:
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.