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Emotion-enhanced classification based on fuzzy reasoning
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DOI：10.1007/s13042-021-01356-y.png)
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Abstract
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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 negativeword 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.
Keywords:
Natural language processing
Sentiment classification
Fuzzy reasoning
Deep learning
Emoticons
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