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Proceedings Paper

Recognizing emotions in chinese text using dictionary and ensemble of classifiers
Author(s): Yanyong Ai; Zhenxiang Chen; Shanshan Wang; Ying Pang
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Paper Abstract

In recent years, subjective texts have shown great application value. As a hot research issues in the field of natural language processing, analysis of emotions in text, has attracted attentions from many scholars and also greatly develops research on the emotional polarity of Chinese texts. This paper presents an emotional classification algorithm combining dictionary and ensemble classifier. Firstly, based on the fusion of multiple dictionaries such as emotional dictionary, degree dictionary, and negative dictionary, output the negative and positive scores of each sentence according to the designed emotion calculation algorithm. Defining the difference value between negative and positive scores as the emotional tendency value, the samples are sorted by the amount of emotional tendency value and samples with the highest emotional tendency value are selected as the training samples. Finally, the ensemble classifier is used to classify the text emotions. Based on six machine learning algorithms including polynomial Bayes, decision tree, random forest, k-nearest neighbor, SVM, and logistic regression, the ensemble classifier aims to achieve the best classification effect and minimize the disadvantages of individual classifiers. The results show that the classification accuracy of the ensemble classifier is better than that of individual classifiers.

Paper Details

Date Published: 26 July 2018
PDF: 7 pages
Proc. SPIE 10828, Third International Workshop on Pattern Recognition, 1082807 (26 July 2018); doi: 10.1117/12.2501916
Show Author Affiliations
Yanyong Ai, Univ. of Jinan (China)
Zhenxiang Chen, Univ. of Jinan (China)
Shanshan Wang, Univ. of Jinan (China)
Ying Pang, Univ. of Jinan (China)

Published in SPIE Proceedings Vol. 10828:
Third International Workshop on Pattern Recognition
Xudong Jiang; Zhenxiang Chen; Guojian Chen, Editor(s)

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