Publication
Title
Automatic emotion classification for interpersonal communication
Author
Abstract
We introduce a new emotion classification task based on Leary's Rose, a framework for interpersonal communication. We present a small dataset of 740 Dutch sentences, outline the annotation process and evaluate annotator agreement. We then evaluate the performance of several automatic classification systems when classifying individual sentences according to the four quadrants and the eight octants of Leary's Rose. SVM-based classifiers achieve average F-scores of up to 51% for 4-way classification and 31% for 8-way classification, which is well above chance level. We conclude that emotion classification according to the Interpersonal Circumplex is a challenging task for both humans and machine learners. We expect classification performance to increase as context information becomes available in future versions of our dataset.
Language
English
Source (book)
Proceedings of the Workshop on Computational Approaches to Subjectivity and Sentiment Analysis (WASSA), ACL-HLT 2011
Publication
Portland, Or. : Association for Computational Linguistics, 2011
ISBN
978-1-937284-06-0
Volume/pages
p. 104-110
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Record
Identification
Creation 02.02.2012
Last edited 09.09.2014
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