@Article{Benites-LazaroGiatGiar:2018:ToMoMe,
               author = "Benites-Lazaro, Lira Luz and Giatti, Leandro and Giarolla, 
                         Ang{\'e}lica",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Universidade de S{\~a}o Paulo (USP)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                title = "Topic modeling method for analyzing social actor discourses on 
                         climate change, energy and food security",
              journal = "Energy Research and Social Science",
                 year = "2018",
               volume = "45",
                pages = "318--330",
                month = "Nov.",
             keywords = "Topic modeling, Latent Dirichlet allocation (LDA), Cluster 
                         analysis, Discourse analysis, Climate change, Energy, Ethanol, 
                         Food security, Machine learning, Natural language processing.",
             abstract = "Debates on climate change, energy and food security concerns have 
                         underscored the need for new methods and tools to explore and 
                         understand the complexity of these relevant issues. In this study, 
                         we used unsupervised probabilistic modeling-latent Dirichlet 
                         allocation (LDA)-to examine the changes in social policy debates 
                         related to ethanol production in Brazil and its relationship with 
                         climate change and food security. We analyze a large amount of 
                         data obtained from Brazilian newspapers, government and business 
                         documents, and the bulletins of nongovernmental organizations and 
                         social movements from 2007 through 2017. The results from the LDA 
                         application allowed us to identify key topics, detect novel 
                         trends, and follow them through time, in addition to exhibiting 
                         the limitations encountered in identifying social actor 
                         discourses. To overcome these limitations, we combine LDA and 
                         discourse analysis to enable the construction of topics, concepts, 
                         and discourses of the actors surrounding the issue of this study. 
                         The findings verify the utility of these techniques by emphasizing 
                         the themes and discourses of the actors involved and by 
                         identifying the determinant positions of the Brazilian actors in 
                         the discussions on ethanol production and its competition with 
                         food security and the contributions of ethanol as a renewable 
                         energy source to mitigate climate change. This finding provides 
                         insights for water-energy-food nexus research.",
                  doi = "10.1016/j.erss.2018.07.031",
                  url = "http://dx.doi.org/10.1016/j.erss.2018.07.031",
                 issn = "2214-6296",
             language = "en",
           targetfile = "benites_topic.pdf",
        urlaccessdate = "02 maio 2024"
}