Polarity Classification of Traffic Related Tweets

Clarissa Castellã Xavier

doi:10.5753/eniac.2018.4417

Clarissa Castellã Xavier UFRGS

DOI: https://doi.org/10.5753/eniac.2018.4417

Resumo

In this paper we present a study about polarity classification of tweets in the traffic domain. Specifically, we use the data in Portuguese language from an account maintained by a traffic management agency. We evaluate the performance of three learning methods: SVM (Support Vector Machine), Naive Bayes and Maximum Entropy. We also explore how the use of balanced vs. unbalanced corpus affects the models behavior. The results show that, in this context, a ML classifier obtains better results than the reported in the literature. In our experiments, SVM trained with a balanced corpus outperforms all tested models, achieving 99% of Accuracy, Average Recall and Average Precision.

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