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      Toward reduction of detrimental effects of hurricanes using a social media data analytic Approach: How climate change is perceived?

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      Climate Risk Management
      Elsevier BV

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          THE POLITICIZATION OF CLIMATE CHANGE AND POLARIZATION IN THE AMERICAN PUBLIC'S VIEWS OF GLOBAL WARMING, 2001-2010

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              Text Classification Algorithms: A Survey

              In recent years, there has been an exponential growth in the number of complex documentsand texts that require a deeper understanding of machine learning methods to be able to accuratelyclassify texts in many applications. Many machine learning approaches have achieved surpassingresults in natural language processing. The success of these learning algorithms relies on their capacityto understand complex models and non-linear relationships within data. However, finding suitablestructures, architectures, and techniques for text classification is a challenge for researchers. In thispaper, a brief overview of text classification algorithms is discussed. This overview covers differenttext feature extractions, dimensionality reduction methods, existing algorithms and techniques, andevaluations methods. Finally, the limitations of each technique and their application in real-worldproblems are discussed.
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                Author and article information

                Journal
                Climate Risk Management
                Climate Risk Management
                Elsevier BV
                22120963
                2023
                2023
                : 39
                : 100480
                Article
                10.1016/j.crm.2023.100480
                6955f5cb-dbeb-4a46-810f-98406cbdb509
                © 2023

                https://www.elsevier.com/tdm/userlicense/1.0/

                http://creativecommons.org/licenses/by-nc-nd/4.0/

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