A rule-based approach to implicit emotion detection in text

Udochukwu Orizu, Yulan He

Research output: Chapter in Book/Published conference outputConference publication


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.

Original languageEnglish
Title of host publicationNatural language processing and information systems
Subtitle of host publication20th international conference on applications of Natural Language to Information Systems, NLDB 2015, Passau, Germany, June 17-19, 2015, proceedings
EditorsChris Biemann, Siegfried Handschuh, André Freitas, et al
Place of PublicationCham (CHE)
Number of pages7
ISBN (Electronic)978-3-319-19581-0
ISBN (Print)978-3-319-19580-3
Publication statusPublished - 4 Jun 2015
Event20th International Conference on Applications of Natural Language to Information Systems - Passau, Germany
Duration: 17 Jun 201519 Jun 2015

Publication series

NameLecture notes in computer science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference20th International Conference on Applications of Natural Language to Information Systems
Abbreviated titleNLDB 2015

Bibliographical note

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-19581-0_17


  • emotion detection
  • implicit emotions
  • OCC model
  • rule-based approach


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