Data mining analysis method of consumer behaviour characteristics based on social media big data
by Tiantian An
International Journal of Web Based Communities (IJWBC), Vol. 18, No. 3/4, 2022

Abstract: In order to solve the problems of low accuracy, high time and small capacity in the current analysis method of consumer behaviour, this paper proposes a method of mining and analysing consumer behaviour feature databased on big data of social media. The big data of social media is preprocessed, and the feature selection model is established to generate the label of social media big data users. k-means algorithm is used to cluster the consumer behaviour feature data, and the multi-dimensional consumption behaviour feature is extracted to construct the hierarchical model of the consumer behaviour feature data. The non-parametric regression method of k-nearest neighbour is used to mining and analysing the characteristics of consumer behaviour. The experimental results show that the accuracy of the proposed method is 92%, the time of feature analysis is only 14 s, and the data analysis capacity is up to 63 T.

Online publication date: Mon, 12-Sep-2022

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Web Based Communities (IJWBC):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com