A machine learning based approach to identify geo-location of Twitter users
| dc.contributor.author | Onan A. | |
| dc.date.accessioned | 2024-07-22T08:10:49Z | |
| dc.date.available | 2024-07-22T08:10:49Z | |
| dc.date.issued | 2017 | |
| dc.description.abstract | Twitter, a popular microblogging platform, has attracted great attention. Twitter enables people from all over the world to interact in an extremely personal way. The immense quantity of user-generated text messages become available on Twitter that could potentially serve as an important source of information for researchers and practitioners. The information available on Twitter may be utilized for many purposes, such as event detection, public health and crisis management. In order to effectively coordinate such activities, the identification of Twitter users' geo-locations is extremely important. Though online social networks can provide some sort of geo-location information based on GPS coordinates, Twitter suffers from geo-location sparseness problem. The identification of Twitter users' geo-location based on the content of send out messages, becomes extremely important. In this regard, this paper presents a machine learning based approach to the problem. In this study, our corpora is represented as a word vector. To obtain a classification scheme with high predictive performance, the performance of five classification algorithms, three ensemble methods and two feature selection methods are evaluated. Among the compared algorithms, the highest results (84.85%) is achieved by AdaBoost ensemble of Random Forest, when the feature set is selected with the use of consistency-based feature selection method in conjunction with best first search. © 2017 ACM. | |
| dc.identifier.DOI-ID | 10.1145/3018896.3018969 | |
| dc.identifier.uri | http://akademikarsiv.cbu.edu.tr:4000/handle/123456789/15377 | |
| dc.language.iso | English | |
| dc.publisher | Association for Computing Machinery | |
| dc.subject | Adaptive boosting | |
| dc.subject | Artificial intelligence | |
| dc.subject | Cloud computing | |
| dc.subject | Data mining | |
| dc.subject | Decision trees | |
| dc.subject | Feature extraction | |
| dc.subject | Internet of things | |
| dc.subject | Learning systems | |
| dc.subject | Location | |
| dc.subject | Natural language processing systems | |
| dc.subject | Classification algorithm | |
| dc.subject | Feature selection methods | |
| dc.subject | Geolocations | |
| dc.subject | Location based | |
| dc.subject | Micro-blogging platforms | |
| dc.subject | On-line social networks | |
| dc.subject | Predictive performance | |
| dc.subject | Text mining | |
| dc.subject | Social networking (online) | |
| dc.title | A machine learning based approach to identify geo-location of Twitter users | |
| dc.type | Conference paper |