Comparison of different machine learning techniques on location extraction by utilizing geo-tagged tweets: A case study
| Autor: | Nazmiye Eligüzel, Cihan Çetinkaya, Türkay Dereli |
|---|---|
| Přispěvatelé: | HKÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümü |
| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Boosting (machine learning) LDA Computer science 0211 other engineering and technologies 02 engineering and technology Machine learning computer.software_genre Latent Dirichlet allocation Naive Bayes classifier symbols.namesake 020901 industrial engineering & automation Artificial Intelligence 021105 building & construction AdaBoost Artificial neural network business.industry Sentiment analysis Tweet Random forest Support vector machine Sentiment ComputingMethodologies_PATTERNRECOGNITION symbols Artificial intelligence business computer Geo-tagged Location extraction Information Systems |
| Zdroj: | Advanced Engineering Informatics. 46:101151 |
| ISSN: | 1474-0346 |
| DOI: | 10.1016/j.aei.2020.101151 |
| Popis: | 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 Naïve 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 Naïve 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. © 2020 Elsevier Ltd |
| Databáze: | OpenAIRE |
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