Topic identification and sentiment trends in Weibo and WeChat content related to intellectual property in China

Zaoli Yang, Qingyang Wu, K. Venkatachalam, Yuchen Li, Bing Xu, Pavel Trojovský

Research output: Contribution to journalArticlepeer-review

Abstract

Intense frictions in global trade have made intellectual property (IP) an important topic of public concern. Meanwhile, new media and online communities have become important platforms for the public to discuss IP issues. Mining the core topics and judging their sentiment status from the public's massive online IP data are important means for the government to formulate and evaluate IP policies, for enterprises to carry out R&D and identify business opportunities. Hence, this study aims to conduct topic identification and sentiment trends in Weibo and WeChat content related to IPs in China by employing a novel ensemble method combining the term frequency inverse document frequency (TF-IDF), TextRank, latent Dirichlet allocation (LDA), the Word2vec model, and attention-based bidirectional long short-term memory (BiLSTM). To be more specific, the text information on IPs in Weibo and WeChat is extracted using the TF-IDF and TextRank algorithms. Then, the probability of keywords in text and their IP topics are obtained based on the LDA and t-SNE models. Sentiment polarity and topic trends are analyzed by the Word2vec model and BiLSTM. The results show that 16 topics related to IP were identified, and most topics presented high levels of positive sentiment; the development trend lines of the two emotions are easily affected by abnormal events, and thus, show obvious fluctuation.
Original languageEnglish
Article number121980
JournalTechnological Forecasting and Social Change
Volume184
Early online date31 Aug 2022
DOIs
Publication statusE-pub ahead of print - 31 Aug 2022

Keywords

  • Ensemble method
  • Intellectual property in China
  • Sentiment analysis
  • Topic identification
  • Weibo and WeChat

ASJC Scopus subject areas

  • Business and International Management
  • Applied Psychology
  • Management of Technology and Innovation

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