Prediction and Classification of User Activities Using Machine Learning Models from Location-Based Social Network Data
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Published:2023-03-09
Issue:6
Volume:13
Page:3517
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ISSN:2076-3417
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Container-title:Applied Sciences
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language:en
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Short-container-title:Applied Sciences
Author:
Khan Naimat Ullah123ORCID, Wan Wanggen12ORCID, Riaz Rabia4ORCID, Jiang Shuitao12, Wang Xuzhi12
Affiliation:
1. School of Communication & Information Engineering, Shanghai University, Shanghai 200444, China 2. Institute of Smart City, Shanghai University, Shanghai 200444, China 3. School of Computer Science, University of Technology Sydney, Ultimo, NSW 2007, Australia 4. Department of CS & IT, University of Azad Jammu and Kashmir, Muzaffarabad 13100, Pakistan
Abstract
The current research has aimed to investigate and develop machine-learning approaches by using the data in the dataset to be applied to classify location-based social network data and predict user activities based on the nature of various locations (such as entertainment). The analysis of user activities and behavior from location-based social network data is often based on venue types, which require the input of data into various categories. This has previously been done through a tedious and time-consuming manual method. Therefore, we proposed a novel approach of using machine-learning models to extract these venue categories. In this study, we used a Weibo dataset as the main source of research and analyzed machine-learning methods for more efficient implementation. We proposed four models based on well-known machine-learning techniques, including the generalized linear model, logistic regression, deep learning, and gradient-boosted trees. We designed, tested, and evaluated these models. We then used various assessment metrics, such as the Receiver Operating Characteristic or Area Under the Curve, Accuracy, Recall, Precision, F-score, and Sensitivity, to show how well these methods performed. We discovered that the proposed machine-learning models are capable of accurately classifying the data, with deep learning outperforming the other models with 99% accuracy, followed by gradient-boosted tree with 98% and 93%, generalized linear model with 90% and 85%, and logistic regression with 86% and 91%, for multiclass distributions and single class predictions, respectively. We classified the data using our machine-learning models into the 10 classes we used in our previous study and predicted tourist destinations among the data to demonstrate the effectiveness of using machine learning for location-based social network data analysis, which is vital for the development of smart city environments in the current technological era.
Funder
Anhui Natural Science Foundation Anhui Key Research and Development Plan Project
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
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