Title
Automatic emotion classification for interpersonal communication Automatic emotion classification for interpersonal communication
Author
Faculty/Department
Faculty of Arts. Linguistics and Literature
Publication type
conferenceObject
Publication
Portland, Or. :Association for Computational Linguistics, [*]
Subject
Linguistics
Source (book)
Proceedings of the Workshop on Computational Approaches to Subjectivity and Sentiment Analysis (WASSA), ACL-HLT 2011
ISBN
978-1-937284-06-0
Carrier
E
Target language
English (eng)
Affiliation
University of Antwerp
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.
Handle