Abstract
Politics is a commonly used domain in Opinion Mining applications, in which opinions may change over time. Nevertheless, the usual approaches for Opinion Mining are not able to deal with the characteristics and the challenges brought by continuous data streams; so, an alternative is the use of techniques such as Active Learning, which labels selected data rather than the entire data set. The Active Learning approach requires the choice of a sampling strategy to select the most valuable instances. However, no study has performed an analysis in order to identify the best strategies for Opinion Mining. In this sense, we evaluated eight Active Learning sampling strategies, from which Entropy achieved the best results. In addition, due to the lack of publicly available stream data sets written in Portuguese, we created and evaluated corpora from Twitter and Facebook about the 2018 Brazilian presidential elections.
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Acknowledgment
Douglas Vitório and Adriano L. I. Oliveira are supported by CNPq (Brazilian Council for Scientific and Technological Development).
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Vitório, D., Souza, E., Oliveira, A.L.I. (2019). Evaluating Active Learning Sampling Strategies for Opinion Mining in Brazilian Politics Corpora. In: Moura Oliveira, P., Novais, P., Reis, L. (eds) Progress in Artificial Intelligence. EPIA 2019. Lecture Notes in Computer Science(), vol 11805. Springer, Cham. https://doi.org/10.1007/978-3-030-30244-3_57
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