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

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dc.authoridEliguzel, Nazmiye/0000-0001-6354-8215
dc.authoridDereli, Turkay/0000-0002-2130-5503
dc.authoridCetinkaya, Cihan/0000-0002-5899-8438
dc.contributor.authorEliguzel, Nazmiye
dc.contributor.authorCetinkaya, Cihan
dc.contributor.authorDereli, Turkay
dc.date.accessioned2025-01-06T17:38:08Z
dc.date.available2025-01-06T17:38:08Z
dc.date.issued2020
dc.description.abstractIn 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.
dc.identifier.doi10.1016/j.aei.2020.101151
dc.identifier.issn1474-0346
dc.identifier.issn1873-5320
dc.identifier.scopus2-s2.0-85090404146
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.aei.2020.101151
dc.identifier.urihttps://hdl.handle.net/20.500.14669/2496
dc.identifier.volume46
dc.identifier.wosWOS:000607575400007
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier Sci Ltd
dc.relation.ispartofAdvanced Engineering Informatics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241211
dc.subjectGeo-tagged
dc.subjectLDA
dc.subjectLocation extraction
dc.subjectMachine learning
dc.subjectSentiment
dc.subjectTweet
dc.titleComparison of different machine learning techniques on location extraction by utilizing geo-tagged tweets: A case study
dc.typeArticle

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