Classification of Traffic Event Tweets in Portuguese Language Using Deep Learning

In urban mobility scope, one of the main current challenges is fast and localized information gathering for decision making. In this sense, social media can be an information extraction source for several tasks, such as traffic control. However, these data have to be well classified in order to ensure that only relevant information is used. Particularly in Portuguese-speaking countries, there are not many works with this approach. Thus, we designed a methodology to identify traffic information from social media using modern techniques for classification. In addition, we generated a dataset, by extracting and manually labeling tweets in Portuguese language, and used deep learning techniques to classify these messages with respect to the traffic domain. Finally, we perform experiments employing different combinations of deep learning architectures for representation and classification, obtaining results of more than 95% on average for accuracy and precision metrics.