@inproceedings{bc5c945795da4c129ef6315fb931e355,
title = "A rule-based approach to implicit emotion detection in text",
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.",
keywords = "emotion detection, implicit emotions, OCC model, rule-based approach",
author = "Udochukwu Orizu and Yulan He",
note = "The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-19581-0_17; 20th International Conference on Applications of Natural Language to Information Systems, NLDB 2015 ; Conference date: 17-06-2015 Through 19-06-2015",
year = "2015",
month = jun,
day = "4",
doi = "10.1007/978-3-319-19581-0_17",
language = "English",
isbn = "978-3-319-19580-3",
series = "Lecture notes in computer science",
publisher = "Springer",
pages = "197--203",
editor = "Chris Biemann and Siegfried Handschuh and Andr{\'e} Freitas and {et al}",
booktitle = "Natural language processing and information systems",
address = "Germany",
}