## 5 Conclusions  
This study proposes a novel approach to LBSN venue-based analysis by the implementation of machine-learning models for the prediction of venue classes used in the research of patterns in the behavior of different LBSN users. The classification task has always been done manually, which is very tiresome and time-consuming as most of the LBSN research is based on big-data analysis comprising thousands and millions of check-in records. There are several robust machine-learning methods that can be used to carry out this task more efficiently and effectively. In this research, we developed four machine-learning models based on famous classification and prediction methods, including the generalized linear model, deep learning, logistic regression, and gradient-boosted trees for our experiments. These data mining techniques on LBSN data are rigorous tasks due to the fact that many features are related to many different domains in various research fields. After careful and systematic filtering, we extracted the feasible input features for the prediction of our targeted class, which followed the training and testing of our developed models. The results revealed that the deep-learning model performs exceptionally well for classifying and predicting venues within the LBSN data, achieving 99% accuracy. The gradient-boosted trees model attained 93% accuracy for our tourism class prediction problem, followed by logistic regression and the generalized linear model, reaching 91% and 85% accuracy, respectively. The machine-learning models perform well, but the research has some limitations. For example, the models must be tested on LBSN data from other platforms and other research domains in order to provide a more generalized solution to the LBSN classification and  
Figure 7: (**a**–**d**) Lift chart of proposed models.  
prediction problem. The presented results can be beneficial in a variety of research fields by specifying and predicting the desired class of venues and users. It can also provide the basis to conduct LBSN data analysis to predict the interests, behavior, and trends of the population within a specific time of the day or day of the week. The use of machine learning for such kind of research can benefit both researchers and end users for better planning, targeted marketing, and development of a smart-city environment. Therefore, the proposed models can have many advantages, both practically and educationally. In future research, we will try to implement these machine-learning models to analyze the behavioral traits of tourists by finding the similarities and differences in Shanghai's local, nonlocal, and international tourists with the help of statistical analysis and density estimation. We will also try to perform in-dept analysis with the proposed machine-learning models to predict the interests and preferences of these tourists with respect to time and days based on the historical data from Location-Based Social Networks.  
Conceptualization, N.U.K. and W.W.; methodology, N.U.K.; software, W.W.; validation, N.U.K. and W.W.; formal analysis, investigation, resources, data curation, visualization, writing--original draft, N.U.K., S.J. and X.W.; writing--review and editing, W.W. and R.R.; supervision, project administration, funding acquisition, W.W. All authors have read and agreed to the published version of the manuscript.  
This research is partially funded by Anhui Natural Science Foundation (No: 1908085 MF178) and Anhui Key Research and Development Plan Project (No. 202104b11020031).  
Weibo has open geo-database, which can be downloaded using its API. More information is available at: [[https://open.weibo.com/wiki/Index](https://open.weibo.com/wiki/Index)]([https://open.weibo.com/wiki/Index](https://open.weibo.com/wiki/Index)) accessed on 1 March 2023.  
The authors declare no conflict of interest.