@InProceedings{CavalinDornCruz:2016:ClLiEv,
               author = "Cavalin, Paulo and Dornelas, Fillipe and Cruz, Sergio",
          affiliation = "{IBM Research} and IBM Research, Universidade Federal Rural do Rio 
                         de Janeiro and {Universidade Federal Rural do Rio de Janeiro}",
                title = "Classification of Life Events on Social Media",
            booktitle = "Proceedings...",
                 year = "2016",
               editor = "Aliaga, Daniel G. and Davis, Larry S. and Farias, Ricardo C. and 
                         Fernandes, Leandro A. F. and Gibson, Stuart J. and Giraldi, Gilson 
                         A. and Gois, Jo{\~a}o Paulo and Maciel, Anderson and Menotti, 
                         David and Miranda, Paulo A. V. and Musse, Soraia and Namikawa, 
                         Laercio and Pamplona, Mauricio and Papa, Jo{\~a}o Paulo and 
                         Santos, Jefersson dos and Schwartz, William Robson and Thomaz, 
                         Carlos E.",
         organization = "Conference on Graphics, Patterns and Images, 29. (SIBGRAPI)",
            publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
              address = "Porto Alegre",
             keywords = "Social Media, Life Events, Classification, Umbalanced datasets.",
             abstract = "In this paper we present an investigation of life event 
                         classification on social media networks. Detecting personal 
                         mentions about life events, such as travel, birthday, wedding, 
                         etc, presents an interesting opportunity to anticipate the offer 
                         of products or services, as well to enhance the demographics of a 
                         given target population. Nevertheless, life event classification 
                         can be seen as an unbalanced classification problem, where the set 
                         of posts that actually mention a life event is significantly 
                         smaller than those that do not. For this reason, the main goal of 
                         this paper is to investigate different types of classifiers, on a 
                         experimental protocol based on datasets containing various types 
                         of life events in both Portuguese and English languages, and the 
                         benefits of over-sampling techniques to improve the accuracy of 
                         these classifiers on these sets. The results demonstrate that a 
                         Logistic Regression may be a poor choice to deal with the original 
                         datasets, but after over-sampling the training set, such 
                         classifier is able to outperform by a significant margin other 
                         classifiers such as Naive Bayes and Nearest Neighbours, which do 
                         not benefit as well from the over-sampled training set in most 
                         cases.",
  conference-location = "S{\~a}o Jos{\'e} dos Campos, SP, Brazil",
      conference-year = "4-7 Oct. 2016",
             language = "en",
                  ibi = "8JMKD3MGPAW/3MC59RH",
                  url = "http://urlib.net/ibi/8JMKD3MGPAW/3MC59RH",
           targetfile = "SibgrapiWIA_LifeEvents_2016_cameraready.pdf",
        urlaccessdate = "2024, Apr. 28"
}