An optimized public opinion communication system in social media networks based on K-means cluster analysis
This study proposes a public opinion monitoring model that combines the K-means clustering algorithm with Particle Swarm Optimization (PSO) to enhance the accuracy and effectiveness of public opinion monitoring on social media. The model's performance across various dissemination indicators is...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844024160641 |
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| author | Mingchao Qi JunQiang Zhao Yan Feng |
| author_facet | Mingchao Qi JunQiang Zhao Yan Feng |
| author_sort | Mingchao Qi |
| collection | DOAJ |
| description | This study proposes a public opinion monitoring model that combines the K-means clustering algorithm with Particle Swarm Optimization (PSO) to enhance the accuracy and effectiveness of public opinion monitoring on social media. The model's performance across various dissemination indicators is studied in detail. Through experiments conducted on social media datasets, the study comprehensively evaluates the model from four dimensions: dissemination speed, scope, depth, and sentiment dissemination effectiveness. The experimental results indicate that the proposed optimization model excels in multiple areas, particularly in dissemination depth and sentiment dissemination effectiveness. Specifically, in the three dimensions of dissemination speed, the proposed model achieves scores of 4.3, 4.2, and 4.4 in initial dissemination speed, decay speed, and peak dissemination speed, respectively. In the dimensions of user coverage, geographic coverage, and platform coverage under dissemination scope, the model scores 4.4, 4.5, and 4.3, demonstrating a broad dissemination capability. Additionally, in the dimensions of hierarchical dissemination depth and key node influence within dissemination depth, the model scores 4.3 and 4.5, indicating excellent performance in multi-level dissemination and key node activation. In sentiment dissemination effectiveness, the model receives scores of 4.4, 4.5, and 4.4 in emotional tendency change, polarity distribution, and diffusion intensity, showcasing its advantages in sentiment classification and dissemination. Sensitivity analysis further validates the model's sensitivity to parameter settings, with experiments showing that reasonable adjustments to parameters such as the K value, PSO inertia weight, and learning factors can reduce the Sum of Squared Errors to 3209.72. Meanwhile, it can improve clustering purity to 0.822 and raise the Rand index to 0.623. Therefore, this study offers an efficient and reliable solution for public opinion monitoring on social media, providing valuable reference significance. |
| format | Article |
| id | doaj-art-ca9e4c2e82054d9cbb1e5d9e339e3b51 |
| institution | Kabale University |
| issn | 2405-8440 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj-art-ca9e4c2e82054d9cbb1e5d9e339e3b512024-12-19T10:55:49ZengElsevierHeliyon2405-84402024-12-011024e40033An optimized public opinion communication system in social media networks based on K-means cluster analysisMingchao Qi0JunQiang Zhao1Yan Feng2Corresponding author.; Xinxiang Medical University, Xinxiang, 453000, ChinaXinxiang Medical University, Xinxiang, 453000, ChinaXinxiang Medical University, Xinxiang, 453000, ChinaThis study proposes a public opinion monitoring model that combines the K-means clustering algorithm with Particle Swarm Optimization (PSO) to enhance the accuracy and effectiveness of public opinion monitoring on social media. The model's performance across various dissemination indicators is studied in detail. Through experiments conducted on social media datasets, the study comprehensively evaluates the model from four dimensions: dissemination speed, scope, depth, and sentiment dissemination effectiveness. The experimental results indicate that the proposed optimization model excels in multiple areas, particularly in dissemination depth and sentiment dissemination effectiveness. Specifically, in the three dimensions of dissemination speed, the proposed model achieves scores of 4.3, 4.2, and 4.4 in initial dissemination speed, decay speed, and peak dissemination speed, respectively. In the dimensions of user coverage, geographic coverage, and platform coverage under dissemination scope, the model scores 4.4, 4.5, and 4.3, demonstrating a broad dissemination capability. Additionally, in the dimensions of hierarchical dissemination depth and key node influence within dissemination depth, the model scores 4.3 and 4.5, indicating excellent performance in multi-level dissemination and key node activation. In sentiment dissemination effectiveness, the model receives scores of 4.4, 4.5, and 4.4 in emotional tendency change, polarity distribution, and diffusion intensity, showcasing its advantages in sentiment classification and dissemination. Sensitivity analysis further validates the model's sensitivity to parameter settings, with experiments showing that reasonable adjustments to parameters such as the K value, PSO inertia weight, and learning factors can reduce the Sum of Squared Errors to 3209.72. Meanwhile, it can improve clustering purity to 0.822 and raise the Rand index to 0.623. Therefore, this study offers an efficient and reliable solution for public opinion monitoring on social media, providing valuable reference significance.http://www.sciencedirect.com/science/article/pii/S2405844024160641Cluster analysisSocial mediaNetwork public opinionData miningK-means algorithm |
| spellingShingle | Mingchao Qi JunQiang Zhao Yan Feng An optimized public opinion communication system in social media networks based on K-means cluster analysis Heliyon Cluster analysis Social media Network public opinion Data mining K-means algorithm |
| title | An optimized public opinion communication system in social media networks based on K-means cluster analysis |
| title_full | An optimized public opinion communication system in social media networks based on K-means cluster analysis |
| title_fullStr | An optimized public opinion communication system in social media networks based on K-means cluster analysis |
| title_full_unstemmed | An optimized public opinion communication system in social media networks based on K-means cluster analysis |
| title_short | An optimized public opinion communication system in social media networks based on K-means cluster analysis |
| title_sort | optimized public opinion communication system in social media networks based on k means cluster analysis |
| topic | Cluster analysis Social media Network public opinion Data mining K-means algorithm |
| url | http://www.sciencedirect.com/science/article/pii/S2405844024160641 |
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