Constructing the Public Opinion Crisis Prediction Model Using CNN and LSTM Techniques Based on Social Network Mining
Autor:
Yan, Lou
; Ren, Zhipeng
; Zhang, Yong
; Tao, Zhonghui
; Zhao, Yizu
Fecha:
07/2024Palabra clave:
Revista / editorial:
International Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI)Citación:
Y. Lou, Z. Ren, Y. Zhang, Z. Tao, Y. Zhao. Constructing the Public Opinion Crisis Prediction Model Using CNN and LSTM Techniques Based on Social Network Mining, International Journal of Interactive Multimedia and Artificial Intelligence, (2024), http://dx.doi.org/10.9781/ijimai.2024.07.005Tipo de Ítem:
article![Open Access Open Access](/themes/Mirage2/images/reunir/openaccess.png)
Resumen:
This research endeavors to address the persistent dissemination of public opinion within social networks, mitigate the propagation of inappropriate content on these platforms, and enhance the overall service quality of social networks. To achieve these objectives, Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) techniques are employed in this research to develop a predictive model for anticipating public opinion crises in social network mining. This model furnishes users with a valuable reference for subsequent decisionmaking processes. The initial phase of this research involves the collection of user behavior data from social networks using IoT technologies, serving as the basis for extensive big data analysis and neural network research. Subsequently, a social network text categorization model is constructed by amalgamating the Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) architecture, elucidating the training procedures of deep learning methodologies within CNN and LSTM networks. The effectiveness of this approach is subsequently validated through comparisons with other deep learning techniques. Based on the obtained results and findings, the CNN-LSTM model demonstrates a noteworthy accuracy rate of 92.19% and an exceptionally low loss value of 0.4075. Of particular significance is the classification accuracy of the CNN-LSTM algorithm within social network datasets, which surpasses that of alternative algorithms, including CNN (by 6.31%), LSTM (by 4.43%), RNN (by 3.51%), Transformer (by 40.29%), and Generative Adversarial Network (GAN) (by 4.49%). This underscores the effectiveness of the CNN-LSTM algorithm in the realm of social network text classification.
Ficheros en el ítem
![icon](/themes/Mirage2/images/reunir/icons/pdf.png)
Nombre: Constructing the Public Opinion Crisis Prediction Model Using CNN and LSTM Techniques Based on Social Network Mining.pdf
Tamaño: 3.173Mb
Formato: application/pdf
Este ítem aparece en la(s) siguiente(s) colección(es)
Estadísticas de uso
Año |
2012 |
2013 |
2014 |
2015 |
2016 |
2017 |
2018 |
2019 |
2020 |
2021 |
2022 |
2023 |
2024 |
Vistas |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
21 |
Descargas |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
9 |
Ítems relacionados
Mostrando ítems relacionados por Título, autor o materia.
-
Towards Multi-perspective Conformance Checking with Fuzzy Sets
Zhang, Sicui; Genga, Laura; Yan, Hui; Nie, Hongchao; Lu, Xudong; Kaymak, Uzay (International Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI), 03/2021)Nowadays organizations often need to employ data-driven techniques to audit their business processes and ensure they comply with laws and internal/external regulations. Failing in complying with the expected process behavior ... -
A Comprehensive Framework for Comparing Textbooks: Insights from the Literature and Experts
Huang, Ronghuai; Tlili, Ahmed; Zhang, Xiangling; Sun, Tianyue; Wang, Junyu; Sharma, Ramesh Chander; Affouneh, Saida; Salha, Soheil Hussein; Altinay, Fahriye; Altinay, Zehra; Olivier, Jako; Jemni, Mohamed; Wang, Yiping; Zhao, Jialu; Burgos, Daniel (Sustainable, 2022)Textbooks are essential components in the learning process. They assist in achieving educational learning outcomes and developing social and cultural values. However, limited studies provide comprehensive frameworks for ... -
Twelve-crystal prototype of Li2MoO4 scintillating bolometers for CUPID and CROSS experiments
Alfonso, K.; Armatol, A.; Augier, C.; Avignone III, F. T.; Azzolini, O.; Balata, M.; Bandac, I.C.; Barabash, A. S.; Bari, G.; Barresi, A.; Baudin, D.; Bellini, F.; Benato, G.; Berest, V.; Beretta, M.; Bettelli, M.; Biassoni, M.; Billard, J.; Boldrini, V.; Branca, A.; Brofferio, C.; Bucci, C.; Calvo-Mozota, José María; Camilleri, J.; Campani, A.; Capelli, C.; Capelli, S.; Cappelli, L.; Cardani, L.; Carniti, P.; Casali, N.; Celi, E.; Chang, C.; Chiesa, D.; Clemenza, M.; Colantoni, I.; Copello, S.; Craft, E.; Cremonesi, O.; Creswick, R. J.; Cruciani, A.; D'Addabbo, A.; D'Imperio, G.; Dabagov, S.; Dafinei, I.; Danevich, F. A.; De Jesus, M.; de Marcillac, P.; Dell'Oro, S.; Di Domizio, S.; Di Lorenzo, S.; Dixon, T.; Dompé, V.; Drobizhev, A.; Dumoulin, L.; Fantini, G.; Faverzani, M.; Ferri, E.; Ferri, F.; Ferroni, F.; Figueroa-Feliciano, E.; Foggetta, L.; Formaggio, J.; Franceschi, A.; Fu, C.; Fu, S.; Fujikawa, B. K.; Gallas, A.; Gascon, J.; Ghislandi, S.; Giachero, A.; Gianvecchio, A.; Girola, M.; Gironi, L.; Giuliani, A.; Gorla, P.; Gotti, C.; Grant, C.; Gras, P.; Guillaumon, P. V.; Gutierrez, T. D.; Han, K.; Hansen, E. V.; Heeger, K. M.; Helis, D. L.; Huang, H. Z.; Ianni, A.; Imbert, L.; Johnston, J.; Juillard, A.; Karapetrov, G.; Keppel, G.; Khalife, H.; Kobychev, V. V.; Kolomensky, Yu. G.; Konovalov, S.I.; Kowalski, R.; Langford, T.; Lefevre, M.; Liu, R.; Liu, Y.; Loaiza, P.; Ma, L.; Madhukuttan, M.; Mancarella, F.; Marrache-Kikuchi, C. A.; Marini, L.; Marnieros, S.; Martinez, M.; Maruyama, R. H.; Ph. Mas; Mayer, D.; Mazzitelli, G.; Mei, Y.; Milana, S.; Morganti, S.; Napolitano, T.; Nastasi, M.; Nikkel, J.; Nisi, S.; Nones, C.; Norman, E. B.; Novosad, V.; Nutini, I.; O'Donnell, T.; Olivieri, E.; Olmi, M.; Ouellet, J. L.; Pagan, S.; Pagliarone, C.; Pagnanini, L.; Pattavina, L.; Pavan, M.; Peng, H.; Pessina, G.; Pettinacci, V.; Pira, C.; Pirro, S.; Poda, D. V.; Polischuk, O. G.; Ponce, I.; Pozzi, S.; Previtali, E.; Puiu, A.; Quitadamo, S.; Ressa, A.; Rizzoli, R.; Rosenfeld, C.; Rosier, P.; Scarpaci, J. A.; Schmidt, B.; Sharma, V.; Shlegel, V. N.; Singh, V.; Sisti, M.; Slocum, P.; Speller, D.; Surukuchi, P. T.; Taffarello, L.; Tomei, C.; Torres, J. A.; Tretyak, V. I.; Tsymbaliuk, A.; Velazquez, M.; Vetter, K. J.; Wagaarachchi, S. L.; Wang, G.; Wang, L.; Wang, R.; Welliver, B.; Wilson, J.; Wilson, K.; Winslow, L. A.; Xue, M.; Yan, L.; Yang, J; Yefremenko, V.; Umatov, V. I.; Zarytskyy, M. M.; Zhang, J.; Zolotarova, A.; Zucchelli, S. (Journal of Instrumentation, 2023)An array of twelve 0.28 kg lithium molybdate (LMO) low-temperature bolometers equipped with 16 bolometric Ge light detectors, aiming at optimization of detector structure for CROSS and CUPID double-beta decay experiments, ...