An intelligent textual corpus big data computing approach for lexicons construction and sentiment classification of public emergency events


Considering the deficiencies in the existing emotional lexicons like too many manual interventions, lack of scalability and ignorance of dependency parsing in emotional computing, this paper first uses Word2Vec, cosine word vector similarity calculation and SO-PMI algorithms to build a public event-oriented Weibo emotional lexicon; then, it proposes a Weibo emotion computing method based on dependency parsing and designs an emotion binary tree based on dependency parsing, and dependency-based emotion calculation rules; and at last, through an experiment, it shows that this emotional lexicon has a wider coverage and higher accuracy than the existing ones, and it also performs a public opinion evolution analysis on an actual public event and the empirical results show that the algorithm is feasible and effective.

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This work was supported by the National Natural Science Foundation of China (Grant No. 71874215), Beijing Natural Science Foundation (9182016), MOE (Ministry of Education in China) Project of Humanities and Social Sciences (17YJAZH120), and Beijing’s Philosophical and Social Science Foundation (Grant No. 13JGC128, 13JGB058). We wish to thank the anonymous reviewers who helped to improve the quality of the paper. The authors gratefully acknowledge the helpful comments and suggestions of the reviewers, which have improved the presentation.

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Correspondence to Yan-chun Zhu.

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Zhang, W., Zhu, Y. & Wang, J. An intelligent textual corpus big data computing approach for lexicons construction and sentiment classification of public emergency events. Multimed Tools Appl 78, 30159–30174 (2019).

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  • Textual corpus
  • Big data
  • Lexicon construction
  • Sentiment computing
  • Public emergency events