Design and Implementation of Data Analysis System of Social Network
Bibliographic Data
| ID | 22253152 |
|---|---|
| Authors | Jinglin Bai (Shandong University, corresponding author) |
| Year | 2022 |
| Volume | 4 |
| Issue | 2 |
| Publication date | 2022-01-01 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | International Journal of Frontiers in Sociology (JOURNAL) |
| Journal identifiers | ISSN: 2706-6827 |
| Publisher | Francis Academic Press Ltd (PUBLISHER) |
| DOI | 10.25236/ijfs.2022.040215 |
| OpenAlex | W4295940058 |
| Language | EN |
| References cited | 12 |
To solve the problem that data collection efficiency of social network analysis technology based on single-mode network structure is low and cannot meet the problem of data information processing of large-scale social network, the social network data analysis system based on the network node centrality theory is proposed. The naive Bayesian classification algorithm is used to improve the traditional data mining mode. The traditional social network data analysis technology is compared with the social network data analysis system proposed in this research from four aspects: multi-platform network data processing response time, data acquisition and processing accuracy, social network data feature condition introduction rate, and data feature condition introduction accuracy. The accuracy of data collection and processing in the traditional social network data analysis system is 89.26%, the response time of multi-platform network data processing is 7.32s, the introduction rate of data feature conditions is 82.65%, and the accuracy rate of data feature conditions is 78.88%. The data processing accuracy of the data analysis system proposed in this research based on the network node centrality theory is 98.99%, the response time of multi-platform network data processing is 1.35s, the data feature condition introduction rate is 97.91%, and the data feature condition introduction accuracy rate is 97.39%. The social network data analysis system proposed in this research is significantly better than the traditional social network data analysis system in all aspects, and its data processing performance is very good especially in multi-platform social network. The results show that the social network data analysis system based on network centrality theory proposed in this research is feasible and can meet the daily application and research requirements of social network data collection, storage, analysis and visual display, which has the advantages of fast data processing speed, high accuracy and wide range of data storage
Bayesian network · Centrality · Data collection · Data mining · Data Processing · Database · Organizational network analysis · Social network analysis · Statistics · Advanced Technologies in Various Fields · AI and Multimedia in Education · Computer Science · E-commerce and Technology Innovations · Engineering · Artificial Intelligence
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| Citation velocity | historical |
|---|---|
| Highly cited | No |