@Article{IbañezRosaGuim:2020:SeAnAp,
               author = "Ibañez, Marilyn Minicucci and Rosa, Reinaldo Roberto and 
                         Guimar{\~a}es, Lamartine N. F.",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto de Estudos 
                         Avan{\c{c}}ados (IEAv)}",
                title = "Sentiment Analysis Applied to Analyze Society’s Emotion in Two 
                         Different Context of Social Media Data",
              journal = "Inteligencia Artificial",
                 year = "2020",
               volume = "23",
               number = "66",
                pages = "66--84",
             keywords = "Machine Learning, Deep Learning, Auto-encoder, Natural Language 
                         Processing, Sentiment Analysis, Social Media.",
             abstract = "In the last few decades, the growth in the use of the Internet has 
                         generated a substantial increase in the circulation of information 
                         on social media. Due to the high interest of several areas of 
                         society in the analysis of these data, a study of better 
                         techniques for the manipulation and understanding of this type of 
                         data is of great importance so that this enormous volume of 
                         information can be interpreted quickly and accurately. Based on 
                         this context, this study shows two approaches of sentiment 
                         analysis to verify the emotion of the population in different 
                         context. The first approach analyses the positive and negative 
                         sentiment about 2018 presidential elections in Brazil considering 
                         data from the Twitter social network. The second approach performs 
                         analysis of data from social media to identify threats sentiment 
                         level of armed conflicts considering data off the conflict between 
                         Syria and the USA in 2017. To achieve this goal, machine learning 
                         techniques such as auto-encoder and deep learning will be 
                         considered in conjunction with NLP text analysis techniques. The 
                         results obtained show the effectiveness of the approaches used in 
                         the classification of sentiment within the domains used according 
                         to the methodology developed for this work.",
                  doi = "10.4114/submission/intartif.vol23iss66pp66-84",
                  url = "http://dx.doi.org/10.4114/submission/intartif.vol23iss66pp66-84",
                 issn = "1137-3601",
             language = "en",
           targetfile = "ibanez_sentiment.pdf",
        urlaccessdate = "01 maio 2024"
}