A Clustering Study of Online Public Opinion Texts on Public Emergency Events Based on Sentence-Level Similarity and Sentiment Analysis
et
25 sept. 2025
À propos de cet article
Publié en ligne: 25 sept. 2025
Reçu: 31 janv. 2025
Accepté: 10 mai 2025
DOI: https://doi.org/10.2478/amns-2025-1018
Mots clés
© 2025 Yaxian Qiu and Hui Han, published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.
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Online public opinion heat value (part)
| Date | Original tweets | Forwarding | Comment | Oi | Oi’ |
|---|---|---|---|---|---|
| 0720 | 812 | 25 | 1628 | 411.8 | 0.06029 |
| 0721 | 3729 | 64 | 4729 | 734.3 | 0.16725 |
| 0722 | 10283 | 2547 | 6248 | 8403.6 | 0.96792 |
| 0723 | 12272 | 1164 | 6027 | 8684.4 | 1 |
| 0724 | 8281 | 75 | 3143 | 3989.1 | 0.65322 |
| 0725 | 7934 | 42 | 1983 | 2933.6 | 0.54855 |
| 0726 | 4032 | 50 | 732 | 512.9 | 0.20316 |
| 0727 | 2224 | 44 | 461 | 83.8 | 0.12149 |
| 0728 | 1034 | 32 | 223 | 33.1 | 0.12883 |
| 0729 | 623 | 10 | 56 | 21.4 | 0.03946 |
| 0730 | 324 | 7 | 30 | 17.5 | 0.02181 |
| 0731 | 182 | 5 | 12 | 16.7 | 0.01486 |
| 0801 | 148 | 3 | 4 | 13.3 | 0.00740 |
| 0802 | 137 | 0 | 2 | 0 | 0.00083 |
| 0803 | 84 | 0 | 1 | 0 | 0.00034 |
Example of results of topic term frequency
| Initial period | Eruption period | Recurrent period | Flat period | ||||
|---|---|---|---|---|---|---|---|
| Keyword | Time | Keyword | Time | Keyword | Time | Keyword | Time |
| Rainstorm | 273 | Be trapped | 583 | Volunteer | 146 | Flood prevention | 37 |
| Forewarning | 231 | Tunnel | 422 | Donation | 93 | Rectify and reform | 30 |
| Zhengzhou | 219 | Message | 394 | Reestablish | 90 | Commemorate | 26 |
| Weather | 210 | Ask for help | 361 | Accountability | 74 | Sponge city | 20 |
| Subway | 187 | Fighting | 347 | Investigation | 69 | Policy | 19 |
| Hydrops | 162 | Casualties | 328 | Insurance | 66 | Present a bouquet | 15 |
| Off-line | 98 | rescue | 287 | Recover | 57 | R.I.P | 14 |
| Notification | 67 | Line 5 | 253 | Drain water | 48 | Recover | 10 |
| Traffic | 52 | Fireman | 218 | Settle | 39 | ||
| Weather bureau | 21 | Submerge | 184 | victim | 31 | ||
