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Comparison of different machine learning techniques on location extraction by utilizing geo-tagged tweets: A case study

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dc.contributor.author Eliguzel, Nazmiye
dc.contributor.author Cetinkaya, Cihan
dc.contributor.author Dereli, Turkay
dc.date.accessioned 2023-01-10T08:29:35Z
dc.date.available 2023-01-10T08:29:35Z
dc.date.issued 2020-10
dc.identifier.citation Eligüzel, N., Çetinkaya, C., & Dereli, T. (2020). Comparison of different machine learning techniques on location extraction by utilizing geo-tagged tweets: A case study. Advanced Engineering Informatics, 46, 101151. https://doi.org/10.1016/j.aei.2020.101151 tr_TR
dc.identifier.issn 1474-0346
dc.identifier.issn 1873-5320
dc.identifier.uri http://openacccess.atu.edu.tr:8080/xmlui/handle/123456789/4115
dc.identifier.uri http://dx.doi.org/10.1016/j.aei.2020.101151
dc.description WOS indeksli yayınlar koleksiyonu. / WOS indexed publications collection. tr_TR
dc.description.abstract In emergencies, Twitter is an important platform to get situational awareness simultaneously. Therefore, information about Twitter users' location is a fundamental aspect to understand the disaster effects. But location extraction is a challenging task. Most of the Twitter users do not share their locations in their tweets. In that respect, there are different methods proposed for location extraction which cover different fields such as statistics, machine learning, etc. This study is a sample study that utilizes geo-tagged tweets to demonstrate the importance of the location in disaster management by taking three cases into consideration. In our study, tweets are obtained by utilizing the earthquake keyword to determine the location of Twitter users. Tweets are evaluated by utilizing the Latent Dirichlet Allocation (LDA) topic model and sentiment analysis through machine learning classification algorithms including the Multinomial and Gaussian Naive Bayes, Support Vector Machine (SVM), Decision Tree, Random Forest, Extra Trees, Neural Network, k Nearest Neighbor (kNN), Stochastic Gradient Descent (SGD), and Adaptive Boosting (AdaBoost) classifications. Therefore, 10 different machine learning algorithms are applied in our study by utilizing sentiment analysis based on location-specific disaster-related tweets by aiming fast and correct response in a disaster situation. In addition, the effectiveness of each algorithm is evaluated in order to gather the right machine learning algorithm. Moreover, topic extraction via LDA is provided to comprehend the situation after a disaster. The gathered results from the application of three cases indicate that Multinomial Naive Bayes and Extra Trees machine learning algorithms give the best results with an F-measure value over 80%. The study aims to provide a quick response to earthquakes by applying the aforementioned techniques. tr_TR
dc.language.iso en tr_TR
dc.relation.ispartofseries 2020;Volume: 46
dc.subject Geo-tagged tr_TR
dc.subject LDA tr_TR
dc.subject Location extraction tr_TR
dc.subject Machine learning tr_TR
dc.subject Sentiment tr_TR
dc.subject Tweet tr_TR
dc.title Comparison of different machine learning techniques on location extraction by utilizing geo-tagged tweets: A case study tr_TR
dc.type Article tr_TR

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