Predicting Location of Tweets Using Machine Learning Approaches
Twitter, one of the most popular microblogging platforms, has tens of millions of active users worldwide, generating hundreds of millions of posts every day. Twitter posts, referred to as “tweets”, the short and the noisy text, bring many challenges with them, such as in the case of some emergency o...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-02-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/13/5/3025 |
| _version_ | 1797615707045756928 |
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| author | Mohammed Alsaqer Salem Alelyani Mohamed Mohana Khalid Alreemy Ali Alqahtani |
| author_facet | Mohammed Alsaqer Salem Alelyani Mohamed Mohana Khalid Alreemy Ali Alqahtani |
| author_sort | Mohammed Alsaqer |
| collection | DOAJ |
| description | Twitter, one of the most popular microblogging platforms, has tens of millions of active users worldwide, generating hundreds of millions of posts every day. Twitter posts, referred to as “tweets”, the short and the noisy text, bring many challenges with them, such as in the case of some emergency or disaster. Predicting the location of these tweets is important for social, security, human rights, and business reasons and has raised noteworthy consideration lately. However, most Twitter users disable the geo-tagging feature, and their home locations are neither standardized nor accurate. In this study, we applied four machine learning techniques named Logistic Regression, Random Forest, Multinomial Naïve Bayes, and Support Vector Machine with and without the utilization of the geo-distance matrix for location prediction of a tweet using its textual content. Our extensive experiments on our vast collection of Arabic tweets From Saudi Arabia with different feature sets yielded promising results with 67% accuracy. |
| first_indexed | 2024-03-11T07:30:29Z |
| format | Article |
| id | doaj.art-2e47cbe0b329479e9be86b8f417214c1 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T07:30:29Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-2e47cbe0b329479e9be86b8f417214c12023-11-17T07:18:16ZengMDPI AGApplied Sciences2076-34172023-02-01135302510.3390/app13053025Predicting Location of Tweets Using Machine Learning ApproachesMohammed Alsaqer0Salem Alelyani1Mohamed Mohana2Khalid Alreemy3Ali Alqahtani4Center for Artificial Intelligence (CAI), King Khalid University, Abha 61421, Saudi ArabiaCenter for Artificial Intelligence (CAI), King Khalid University, Abha 61421, Saudi ArabiaCenter for Artificial Intelligence (CAI), King Khalid University, Abha 61421, Saudi ArabiaCenter for Artificial Intelligence (CAI), King Khalid University, Abha 61421, Saudi ArabiaCenter for Artificial Intelligence (CAI), King Khalid University, Abha 61421, Saudi ArabiaTwitter, one of the most popular microblogging platforms, has tens of millions of active users worldwide, generating hundreds of millions of posts every day. Twitter posts, referred to as “tweets”, the short and the noisy text, bring many challenges with them, such as in the case of some emergency or disaster. Predicting the location of these tweets is important for social, security, human rights, and business reasons and has raised noteworthy consideration lately. However, most Twitter users disable the geo-tagging feature, and their home locations are neither standardized nor accurate. In this study, we applied four machine learning techniques named Logistic Regression, Random Forest, Multinomial Naïve Bayes, and Support Vector Machine with and without the utilization of the geo-distance matrix for location prediction of a tweet using its textual content. Our extensive experiments on our vast collection of Arabic tweets From Saudi Arabia with different feature sets yielded promising results with 67% accuracy.https://www.mdpi.com/2076-3417/13/5/3025location predictionlocation extractionTwitterArabic tweetssocial mediacomputational linguistics |
| spellingShingle | Mohammed Alsaqer Salem Alelyani Mohamed Mohana Khalid Alreemy Ali Alqahtani Predicting Location of Tweets Using Machine Learning Approaches Applied Sciences location prediction location extraction Arabic tweets social media computational linguistics |
| title | Predicting Location of Tweets Using Machine Learning Approaches |
| title_full | Predicting Location of Tweets Using Machine Learning Approaches |
| title_fullStr | Predicting Location of Tweets Using Machine Learning Approaches |
| title_full_unstemmed | Predicting Location of Tweets Using Machine Learning Approaches |
| title_short | Predicting Location of Tweets Using Machine Learning Approaches |
| title_sort | predicting location of tweets using machine learning approaches |
| topic | location prediction location extraction Arabic tweets social media computational linguistics |
| url | https://www.mdpi.com/2076-3417/13/5/3025 |
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