CONFERENCE PROCEEDINGS
Advanced Search
Home > Proceedings > Volume 12260 > Article
Open Access Paper
24 May 2022 Message board information extraction based on LDA and RNN
Lili Meng, Zhaoshun Wang
Author Affiliations +
Proceedings Volume 12260, International Conference on Computer Application and Information Security (ICCAIS 2021); 122600T (2022) https://doi.org/10.1117/12.2637403
Event: International Conference on Computer Application and Information Security (ICCAIS 2021), 2021, Wuhan, China
Abstract
The message board text put forward by netizens on a certain issue is a suggestion or opinion, which is sparse and emotional. The traditional LDA model cannot solve the sparsity problem of the short message and ignores the emotional factor. In order to solve the above problems, a message board information extraction method based on LDA model and RNN model is proposed. First, eigenvalues are introduced to classify the text to solve the sparsity problem based on LDA model. Second, RNN model is used to realize the emotional features on the basis of message text vectorization. The experiment shows that the LDA model with eigenvalues has better topic extraction ability compared with the traditional model, and with the fusion of the RNN model, it can comprehensively display the potential information of the message text. As a result, the proposed method achieves information extraction maximize.
© (2022) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Citation Download Citation
Lili Meng and Zhaoshun Wang "Message board information extraction based on LDA and RNN", Proc. SPIE 12260, International Conference on Computer Application and Information Security (ICCAIS 2021), 122600T (24 May 2022); https://doi.org/10.1117/12.2637403
PROCEEDINGS
 9 PAGES
DOWNLOAD PAPER SAVE TO MY LIBRARY
GET CITATION
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Data modeling
Process modeling
Analytical research
Neural networks
Data acquisition
Internet
Associative arrays
RELATED CONTENT
Subscribe to Digital Library
Receive Erratum Email Alert
Back to Top