Constructing the Public Opinion Crisis Prediction Model Using CNN and LSTM Techniques Based on Social Network Mining.

Yan Lou; Zhipeng Ren; Yong Zhang; Zhonghui Tao; Yiwu Zhao

doi:10.9781/ijimai.2024.07.005

Authors

Yan Lou Changchun University of Science and Technology
Zhipeng Ren Changchun University of Science and Technology
Yong Zhang Changchun University of Science and Technology
Zhonghui Tao Changchun University of Science and Technology
Yiwu Zhao Changchun University of Science and Technology

DOI:

https://doi.org/10.9781/ijimai.2024.07.005

Keywords:

Convolutional Neural Network, Deep Learning, Inappropriate Remarks, Internet of things, Long Short-Term Memory, Social Network

Supporting Agencies

This research was supported by the Natural Science Fund of Jilin Province. (Grant No.20220402014GH).

Abstract

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.

Downloads

Download data is not yet available.

References

F. Wei, S. Zeadally, P. Vijayakumar, N. Kumar, and D. He, “An intelligent terminal based privacy-preserving multi-modal implicit authentication protocol for internet of connected vehicles,” IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 7, pp. 3939-3951, 2020.

W. Wang, N. Kumar, J. Chen, Z. Gong, X. Kong, W. Wei, and H. Gao, “Realizing the potential of the internet of things for smart tourism with 5G and AI,” IEEE Network, vol. 34, no. 6, pp. 295-301, 2020.

A. Shmatko, S. Barykin, S. Sergeev, and A. Thirakulwanich, “Modeling a logistics hub using the digital footprint method—the implication for open innovation engineering,” Journal of Open Innovation: Technology, Market, and Complexity, vol. 7, no. 1, pp. 59, 2021.

Z. S. Ageed, S. R. Zeebaree, M. A. Sadeeq, M. B. Abdulrazzaq, B. W. Salim, A. A. Salih, and A. M. Ahmed, “A state of art survey for intelligent energy monitoring systems,” Asian Journal of Research in Computer Science, vol. 8, no. 1, pp. 46-61, 2021.

L. Nie, Y. Wu, X. Wang, L. Guo, G. Wang, X. Gao, and S. Li, “Intrusion detection for secure social internet of things based on collaborative edge computing: A generative adversarial network-based approach,” IEEE Transactions on Computational Social Systems, vol. 9, no. 1, pp. 134-145, 2021.

Z. Guo, K. Yu, Y. Li, G. Srivastava, and J. C. W. Lin, “Deep learningembedded social internet of things for ambiguity-aware social recommendations,” IEEE Transactions on Network Science and Engineering, vol. 9, no. 3, pp. 1067-1081, 2021.

H. Zhang, W. Qu, H. Long, M. Chen, “The Intelligent Advertising Image Generation using Generative Adversarial Networks and Vision Transformer: A Novel Approach in Digital Marketing,” Journal of Organizational and End User Computing, vol. 36, no. 1, pp.1-26, 2024.

Z. Guo and H. Wang, “A deep graph neural network-based mechanism for social recommendations,” IEEE Transactions on Industrial Informatics, vol. 17, no. 4, pp. 2776-2783, 2020.

M. A. Al-Garadi, A. Mohamed, A. K. Al-Ali, X. Du, I. Ali, and M. Guizani, “A survey of machine and deep learning methods for internet of things (IoT) security,” IEEE Communications Surveys & Tutorials, vol. 22, no. 3, pp. 1646-1685, 2020.

R. A. Khalil, N. Saeed, M. Masood, Y. M. Fard, M. S. Alouini, and T. Y. AlNaffouri, “Deep learning in the industrial internet of things: Potentials, challenges, and emerging applications,” IEEE Internet of Things Journal, vol. 8, no. 14, pp. 11016-11040, 2021.

M. Andronie, G. Lăzăroiu, M. Iatagan, C. Uță, R. Ștefănescu, and M. Cocoșatu, “Artificial intelligence-based decision-making algorithms, internet of things sensing networks, and deep learning-assisted smart process management in cyber-physical production systems,” Electronics, vol. 10, no. 20, p. 2497, 2021.

M. S. Rahman, N. C. Peeri, N. Shrestha, R. Zaki, U. Haque, and S. H. Ab Hamid, “Defending against the Novel Coronavirus (COVID-19) outbreak: How can the Internet of Things (IoT) help to save the world?,” Health Policy and Technology, vol. 9, no. 2, pp. 136-138, 2020.

R. Vinayakumar, M. Alazab, S. Srinivasan, Q. V. Pham, S. K. Padannayil, and K. Simran, “A visualized botnet detection system based deep learning for the internet of things networks of smart cities,” IEEE Transactions on Industry Applications, vol. 56, no. 4, pp. 4436-4456, 2020.

M. Humayun, N. Z. Jhanjhi, A. Alsayat, and V. Ponnusamy, “Internet of things and ransomware: Evolution, mitigation and prevention,” Egyptian Informatics Journal, vol. 22, no. 1, pp. 105-117, 2021.

Z. Lv, L. Qiao, J. Li, and H. Song, “Deep-learning-enabled security issues in the internet of things,” IEEE Internet of Things Journal, vol. 8, no. 12, pp. 9531-9538, 2020.

L. U. Khan, W. Saad, Z. Han, E. Hossain, and C. S. Hong, “Federated learning for internet of things: Recent advances, taxonomy, and open challenges,” IEEE Communications Surveys & Tutorials, vol. 23, no. 3, pp. 1759-1799, 2021.

S. Siboni, V. Sachidananda, Y. Meidan, M. Bohadana, Y. Mathov, S. Bhairav, and Y. Elovici, “Security testbed for Internet-of-Things devices,” IEEE Transactions on Reliability, vol. 68, no. 1, pp. 23-44, 2019.

I. H. Sarker, “Machine learning: Algorithms, real-world applications and research directions,” SN Computer Science, vol. 2, no. 3, p. 160, 2021.

K. Yu, L. Tan, S. Mumtaz, S. Al-Rubaye, A. Al-Dulaimi, A. K. Bashir, and F. A. Khan, “Securing critical infrastructures: deep-learning-based threat detection in IIoT,” IEEE Communications Magazine, vol. 59, no. 10, pp. 76-82, 2021.

P. Muñoz, E. Doñaque, A. Larrañaga, and J. Martínez Torres, “TourismRelated Placeness Feature Extraction from Social Media Data Using Machine Learning Models,” International Journal of Interactive Multimedia and Artificial Intelligence, vol. 8, no. 4, pp. 176-181, 2023.

S. Arroni, Y. Galán, X. Guzmán-Guzmán, E. R. Núñez-Valdez, and A. Gómez, “Sentiment Analysis and Classification of Hotel Opinions in Twitter With the Transformer Architecture,” International Journal of Interactive Multimedia and Artificial Intelligence, vol. 8, no. 1, pp. 53-63, 2023.

X. Zhou, W. Liang, I. Kevin, K. Wang, H. Wang, L. T. Yang, and Q. Jin, “Deep-learning-enhanced human activity recognition for Internet of healthcare things,” IEEE Internet of Things Journal, vol. 7, no. 7, pp. 6429- 6438, 2020.

X. Wang, C. Wang, X. Li, V. C. Leung, and T. Taleb, “Federated deep reinforcement learning for internet of things with decentralized cooperative edge caching,” IEEE Internet of Things Journal, vol. 7, no. 10, pp. 9441-9455, 2020.

M. Lombardi, F. Pascale, and D. Santaniello, “Internet of things: A general overview between architectures, protocols and applications,” Information, vol. 12, no. 2, pp. 87, 2021.

B. B. Gupta and M. Quamara, “An overview of Internet of Things (IoT): Architectural aspects, challenges, and protocols,” Concurrency and Computation: Practice and Experience, vol. 32, no. 21, e4946, 2020.

Javaid, M., & Khan, I. H. (2021). Internet of Things (IoT) enabled healthcare helps to take the challenges of COVID-19 Pandemic. Journal of Oral Biology and Craniofacial Research, 11(2), 209-214.

G. Lou and H. Shi, “Face image recognition based on convolutional neural network,” China Communications, vol. 17, no. 2, pp. 117-124, 2020.

N. Lu, G. Wu, Z. Zhang, Y. Zheng, Y. Ren, and K. K. R. Choo, “Cyberbullying detection in social media text based on character-level convolutional neural network with shortcuts,” Concurrency and Computation: Practice and Experience, vol. 32, no. 23, e5627, 2020.

H. Hu, Z. Liu, and J. An, “Mining mobile intelligence for wireless systems: a deep neural network approach,” IEEE Computational Intelligence Magazine, vol. 15, no. 1, pp. 24-31, 2020.

K. Kumari and J. P. Singh, “Identification of cyberbullying on multi-modal social media posts using genetic algorithm,” Transactions on Emerging Telecommunications Technologies, vol. 32, no. 2, e3907, 2021.

M. Umer, I. Ashraf, A. Mehmood, S. Kumari, S. Ullah, and G. Sang Choi, “Sentiment analysis of tweets using a unified convolutional neural network-long short-term memory network model,” Computational Intelligence, vol. 37, no. 1, pp. 409-434, 2021.

I. Priyadarshini and C. Cotton, “A novel LSTM–CNN–grid searchbased deep neural network for sentiment analysis,” The Journal of Supercomputing, vol. 77, no. 12, pp. 13911-13932, 2021.

Y. Bao, Z. Huang, L. Li, Y. Wang, and Y. Liu, “A BiLSTM-CNN model for predicting users’ next locations based on geotagged social media,” International Journal of Geographical Information Science, vol. 35, no. 4, pp. 639-660, 2021.

H. Kang, H. Wu, and X. Zhang, “Generative text steganography based on LSTM network and attention mechanism with keywords,” Electronic Imaging, vol. 2020, no. 4, pp. 291-1, 2020.

X. He, P. Tai, H. Lu, X. Huang, and Y. Ren, “A biomedical event extraction method based on fine-grained and attention mechanism,” BMC Bioinformatics, vol. 23, no. 1, pp. 1-17, 2022.

N. C. Dang, M. N. Moreno-García, and F. De la Prieta, “Sentiment analysis based on deep learning: A comparative study,” Electronics, vol. 9, no. 3, pp. 483, 2020.

R. Saxena, S. P. Pati, A. Kumar, M. Jadeja, P. Vyas, V. Bhateja, and J. C. W. Lin, “An Efficient Bet-GCN Approach for Link Prediction,” International Journal of Interactive Multimedia and Artificial Intelligence, vol. 8, no. 1, pp. 38-52, 2023.

S. Arroni, Y. Galán, X. Guzmán-Guzmán, E. R. Núñez-Valdez, and A. Gómez, “Sentiment Analysis and Classification of Hotel Opinions in Twitter With the Transformer Architecture,” International Journal of Interactive Multimedia and Artificial Intelligence, vol. 8, no. 1, pp. 53-63, 2023.