Title: A semantic retrieval model of social media data based on statistical theory

Authors: Fuwang Li

Addresses: Department of Mechanical and Electronic Engineering, Xinxiang Vocational and Technical College, Xinxiang, 453006, China

Abstract: Aiming at the problems of low retrieval accuracy and efficiency in semantic retrieval model of social media data, this paper studies semantic retrieval model of social media data based on statistical theory. Statistical theory and ontology of semantic retrieval information of social media data are analysed to complete the labelling process of retrieval information. The semantic retrieval model of social media data is constructed by calculating the similarity of semantic distance and information amount and using statistical theory. Experimental results show that the recall rate of the proposed method is as high as 94%, and the accuracy is as high as 92%, both higher than other methods, and the retrieval time is only 18.2 s. Therefore, the semantic retrieval effect of social media data is good, and the semantic retrieval accuracy and efficiency of social media data are effectively improved.

Keywords: statistical theory; statistical language model; social media data; semantic retrieval; retrieval model.

DOI: 10.1504/IJWBC.2024.136657

International Journal of Web Based Communities, 2024 Vol.20 No.1/2, pp.51 - 62

Received: 01 Mar 2022
Accepted: 09 Jun 2022
Published online: 15 Feb 2024 *

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