| Autor: |
WANG Jian, PENG Yu-qi, ZHAO Yu-fei, YANG Jian |
| Jazyk: |
čínština |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
Jisuanji kexue, Vol 49, Iss 8, Pp 279-293 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
1002-137X |
| DOI: |
10.11896/jsjkx.220300099 |
| Popis: |
With the rapid development of social media platforms,public opinion information can be widely disseminated in a very short period of time.If the information of public opinion is not managed and controlled,it will pose a great threat to the network environment and even the social environment.Information extraction technology has become the first and the most significant step in public opinion analysis and management due to its semantization and accuracy.Over the last few years,with the development of deep learning,its ability to automatically learn potential features and combine these features has dramatically improved the accuracy of each sub-task of information extraction.This paper systematically composes and summarizes the methods of extracting information by combining the characteristics of social media public opinion and deep learning technology.Firstly,we sort out the organization of public opinion information in social networks,elaborate the framework and evaluation indexes of public opinion information extraction.Then we conduct a comprehensive review and analysis of existing deep learning-based public opinion information extraction models,discuss the applicability and limitations of existing methods.Finally,the future research trends is prospected. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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