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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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&apos;s massive online IP data are important means for the government to formulate and evaluate IP policies, for enterprises to carry out R&amp;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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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&apos;s massive online IP data are important means for the government to formulate and evaluate IP policies, for enterprises to carry out R&amp;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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

50204 - Business and management

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Technological Forecasting and Social Change

ISSN

0040-1625

e-ISSN

1873-5509

Svazek periodika

184

Číslo periodika v rámci svazku

NOVEMBER

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

20

Strana od-do

"Article Number: 121980"

Kód UT WoS článku

000854011400003

EID výsledku v databázi Scopus

2-s2.0-85137076813