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International Journal of Machine Learning and Cybernetics

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26-06-2021 | Original Article

Emotion-enhanced classification based on fuzzy reasoning

Authors: Ruiteng Yan, Yan Yu, Dong Qiu

Published in: International Journal of Machine Learning and Cybernetics | Issue 3/2022

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Abstract

Texts and emoticons expressing sentiment can be used to analyse emotion. In an Internet environment, emoticons are frequently used, which have explicated information for emotion analysis. Considering the characteristics of short texts including sparseness, non-standardization and ambiguities in a subject, two models based on word embedding, emotion-dictionary and fuzzy reasoning are proposed: the low-dimensional hybrid feature model and the emotion-enhanced inference model. The low-dimensional hybrid feature model includes the number of emoticons, the emotion-word number and the negative-word number in a text. The emotion-enhanced reference model includes some fuzzy reasoning rules and a variety of the combinations of emotion-words, negative-words, and question marks and exclamation points. The validity of the model has been verified based on Douyin reviews and the data of the 2nd CCF Conference on Natural Language Processing and Chinese Computing (NLPCC 2013), where the average accuracy rate on Douyin reviews achieved is \(89.16\%\). Through the comparative experiment, the results show that the models are more effective in ultra-short emotion text classification than the comparison models.

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Title: Emotion-enhanced classification based on fuzzy reasoning
Authors: Ruiteng Yan
Yan Yu
Dong Qiu
Publication date: 26-06-2021
Publisher: Springer Berlin Heidelberg
Published in: International Journal of Machine Learning and Cybernetics / Issue 3/2022
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI: https://doi.org/10.1007/s13042-021-01356-y

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