Modeling and analyzing a public opinion influence method with K-adaboost
| Autor: | Sida Yuan |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Computer science
business.industry k-means clustering Statistical and Nonlinear Physics 02 engineering and technology Condensed Matter Physics Public opinion computer.software_genre 01 natural sciences 010305 fluids & plasmas ComputingMethodologies_PATTERNRECOGNITION Order (business) Ordinary differential equation 0103 physical sciences 0202 electrical engineering electronic engineering information engineering Influence analysis 020201 artificial intelligence & image processing Social media AdaBoost Data mining business computer |
| Zdroj: | International Journal of Modern Physics B. 34:2050257 |
| ISSN: | 1793-6578 0217-9792 |
| DOI: | 10.1142/s0217979220502574 |
| Popis: | In order to solve the low efficiency of public opinion influence analysis of social media, a new public opinion influence algorithm K-adaboost has been proposed in this paper according to adaboost and K-means algorithms. We first group the training samples and calculate the clustering center of all types of users in the group using the K-means algorithm, and then train the weak classifier of public opinion data and confirm the influence of public opinion on all types of users using the adaboost algorithm, so as to get the total influence of public opinions. Finally, we compare and analyze the performance of K-adaboost, K-means and adaboost algorithms through simulation experiments. The results show that K-adaboost has good adaptability in convergence time and accuracy. |
| Databáze: | OpenAIRE |
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