融合Skip-gram与R-SOPMI的教育领域情感词典构建

doi:10.3969/j.issn.0255-8297.2023.05.012

Abstract

Abstract: This paper presents a method for constructing a fine-grained Sentiment Lexicon in Education to address specific emotional issues in sentiment analysis of educational feedback texts. First, we construct an educational domain corpus, which contains emotional features in both formal and informal domains. Second, a fusion-based method is proposed to construct a domain Sentiment Lexicon by identifying linguistic probability features and statistical probability features of words through sentiment classification. The proposed repetitive semantic orientation pointwise mutual information (R-SOPMI) algorithm enhances SO-PMI for sentiment classification, enabling co-occurrence multi-category sentiment classification. Finally, a fine-grained Sentiment Lexicon in the field of education is obtained, and the dictionary expands to 39 138 emotional words. Experiment results show that except for “anger”, the F1 of the emotion category of the constructed educational field emotion dictionary is all higher than 78.09%. Compared with a general dictionary, the Macro_Precision, Macro_Recall and Macro_F1 increased by 21.95%, 2.50% and 13.01%, respectively. The fusion feature method effectively extracts domain features, facilitating the construction of a comprehensive fine-grained domain dictionary.

Key words: Sentiment Lexicon, sentiment classification, Word2vec, fusion features

CLC Number:

TP391

CHEN Jun, XI Ningli, LI Jiamin, WAN Xiaorong. Constructing Sentiment Lexicon in the Education Field by Integrating Skip-Gram and R-SOPMI[J]. Journal of Applied Sciences, 2023, 41(5): 870-880.

References

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Constructing Sentiment Lexicon in the Education Field by Integrating Skip-Gram and R-SOPMI

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