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

Abstract

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.