Applied Mathematics and Nonlinear Sciences
Journal license

Journal

Applied Mathematics and Nonlinear Sciences


Volume
& Issue

Volume 9, Issue 1


Published
on

May 3, 2024


Pages


DOI

Article

The role of social media data analytics in rural tourism market trend forecasting

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Authors

Zhe Zhang Affiliation:
School of Management, Dalian Polytechnic University, Dalian, Liaoning, 116034, China.
and Minghua Dai Affiliation:
School of Management, Dalian Polytechnic University, Dalian, Liaoning, 116034, China.


Abstract

The rapid development of social media provides new means for market trend and quotation prediction. This paper realizes market trend prediction by performing TF-IDF keyword extraction and vectorization on social media data, improving the deep typical correlation analysis to realize semantic mining, and constructing a consumer intention mining method based on social media data. The rural tourism market trend prediction for Haikou City, Hainan Province, China focuses on examining the number of tourists and the economic income of rural tourism. The future number of inbound tourists and the total number of tourists in Haikou City will still show an upward trend overall, with the total number of tourists predicted to reach 73,641,100 in 2028. With the continuous development of rural tourism, the tourism economy of Haikou City reaches 67.068 billion in 2022, an increase of 259.67% from 2008, and is predicted to double in 2027, and is expected to reach 148.282 billion in 2028. The number of overseas tourists and foreign exchange earnings from rural tourism will continue to increase.


Keywords

TF-IDF, Deep typical correlation analysis, Semantic mining, Rural tourism, Consumption intention mining, 62-07


Citation

Zhang, Z. & Dai, M. (2024). The role of social media data analytics in rural tourism market trend forecasting. Applied Mathematics and Nonlinear Sciences, 9(1). https://doi.org/10.2478/amns-2024-0953

Published by: Engineering Journals

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