  Twitter is a microblogging service for sending short, public text messages
(tweets) that has recently received more attention in scientific comunity. In
the works of Sasaki et al. (2010) and Earle et al., (2011) the authors explored
the real-time interaction on Twitter for detecting natural hazards (e.g.,
earthquakes, typhoons) baed on users' tweets. An inherent challenge for such an
application is the natural language processing (NLP), which basically consists
in converting the words in number (vectors and tensors) in order to
(mathematically/ computationally) make predictions and classifications.
Recently advanced computational tools have been made available for dealing with
text computationally. In this report we implement a NLP machine learning with
TensorFlow, an end-to-end open source plataform for machine learning
applications, to process and classify evenct based on files containing only
text.
