Topic identification and sentiment trends in Weibo and WeChat content related to intellectual property in China
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18470%2F22%3A50019423" target="_blank" >RIV/62690094:18470/22:50019423 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S0040162522005017?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0040162522005017?via%3Dihub</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.techfore.2022.121980" target="_blank" >10.1016/j.techfore.2022.121980</a>
Alternative languages
Result language
angličtina
Original language name
Topic identification and sentiment trends in Weibo and WeChat content related to intellectual property in China
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
50204 - Business and management
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Technological Forecasting and Social Change
ISSN
0040-1625
e-ISSN
1873-5509
Volume of the periodical
184
Issue of the periodical within the volume
NOVEMBER
Country of publishing house
US - UNITED STATES
Number of pages
20
Pages from-to
"Article Number: 121980"
UT code for WoS article
000854011400003
EID of the result in the Scopus database
2-s2.0-85137076813