@InCollection{IbaņezRosaGuim:2022:ApSeAn,
               author = "Ibaņez, Marilyn Minicucci and Rosa, Reinaldo Roberto and 
                         Guimar{\~a}es, Lamartine Nogueira Frutuoso",
                title = "Applying sentiment analysis techniques in social media data about 
                         threat of armed conflicts using two times series models",
            booktitle = "Handbook of research on artificial intelligence applications in 
                         literary works and social media",
            publisher = "IGI Global",
                 year = "2022",
               editor = "Keikhosrokian, P. and Asl, M. P.",
                pages = "220--253",
             keywords = "social media, time series.",
             abstract = "The growing cases of armed conflicts over the past couple of 
                         decades have dramatically affected social landscapes and people 's 
                         lives across the globe, urging everyone to find ways to minimize 
                         the negative consequences of the conflicts. Social media provides 
                         an inexhaustible data source that can be used in understanding the 
                         evolution of such conflicts. This chapter focuses on Syria-USA and 
                         Iran-USA relations to presents an approach to armed conflict 
                         analysis and examines the Russia-Ukraine conflicts by performing 
                         sentiment analysis on the text dataset as well as on a vocabulary 
                         data. All conflicts generate a social media news threat time 
                         series (TTS) that is used as input to the P-model algorithm to 
                         generate the endogenous time series. The following uses the TTS 
                         and endogenous time series for both conflicts as input to the 
                         deep-learning-LSTM neural network. Finally, this chapter compares 
                         the prediction result of the Russia- Ukraine TTS analysis with the 
                         Russia- Ukraine endogenous series using the P-model algorithm.",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Tecnol{\'o}gico de Aeron{\'a}utica (ITA)}",
                  doi = "10.4018/978-1-6684-6242-3.ch011",
                  url = "http://dx.doi.org/10.4018/978-1-6684-6242-3.ch011",
                 isbn = "978-166846244-7, 1668462427, 978-166846242-3",
             language = "en",
        urlaccessdate = "12 maio 2024"
}