A rule-based approach to implicit emotion detection in text

Orizu, Udochukwu and He, Yulan (2015). A rule-based approach to implicit emotion detection in text. IN: Natural language processing and information systems. Biemann, Chris; Handschuh, Siegfried; Freitas, André and et al (eds) Lecture notes in computer science . DEU: Springer.

Abstract

Most research in the area of emotion detection in written text focused on detecting explicit expressions of emotions in text. In this paper, we present a rule-based pipeline approach for detecting implicit emotions in written text without emotion-bearing words based on the OCC Model. We have evaluated our approach on three different datasets with five emotion categories. Our results show that the proposed approach outperforms the lexicon matching method consistently across all the three datasets by a large margin of 17–30% in F-measure and gives competitive performance compared to a supervised classifier. In particular, when dealing with formal text which follows grammatical rules strictly, our approach gives an average F-measure of 82.7% on “Happy”, “Angry-Disgust” and “Sad”, even outperforming the supervised baseline by nearly 17% in F-measure. Our preliminary results show the feasibility of the approach for the task of implicit emotion detection in written text.

Publication DOI: https://doi.org/10.1007/978-3-319-19581-0_17
Divisions: Engineering & Applied Sciences > Computer Science
Engineering & Applied Sciences > Systems analytics research institute (SARI)
Additional Information: The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-19581-0_17
Event Title: 20th International Conference on Applications of Natural Language to Information Systems
Event Type: Other
Event Dates: 2015-06-17 - 2015-06-19
Uncontrolled Keywords: emotion detection,implicit emotions,OCC model,rule-based approach,Computer Science(all),Theoretical Computer Science
Full Text Link: http://link.spr ... -319-19581-0_17
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
Published Date: 2015-06-04
Authors: Orizu, Udochukwu
He, Yulan ( 0000-0003-3948-5845)

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Version: Accepted Version


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