Abstract
The goal of this paper is to compare existing sentiment analysis models, namely Doc2Vec and Recursive Neural Tensor Network, when applied to a skewed class corpus. Such setting is not uncommon, but the literature lacks results on it. We used two techniques to create more balance between classes: under-sampling and over-sampling the target corpora. Doc2Vec achieved the best result overall on the skewed classes, but performed poorly over small and sampled configurations. RNTN achieved the best result on the over-sampled corpus. The Naive Bayes baseline was not surpassed in the under-sampled corpus with Pos/Neg classes, which was the smallest corpus configuration.
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Acknowledgments
This work was partially supported by national funds through Fundação para a Ciência e a Tecnologia (FCT) with reference UID/CEC/50021/2013.
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Brum, H., Araujo, F., Kepler, F. (2016). Sentiment Analysis for Brazilian Portuguese over a Skewed Class Corpora. In: Silva, J., Ribeiro, R., Quaresma, P., Adami, A., Branco, A. (eds) Computational Processing of the Portuguese Language. PROPOR 2016. Lecture Notes in Computer Science(), vol 9727. Springer, Cham. https://doi.org/10.1007/978-3-319-41552-9_14
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