Abstract
Studies in sentiment analysis have examined how different features are effective in identifying subjective sentences. Several studies on sentiment analysis exist in the literature that have already performed some evaluation of NLP classifications. However, the vast majority of them did not handle texts in the Brazilian Portuguese language, and there is no one to consider the combination of sets of text features of NLP tasks with classifiers. Therefore, in our investigation, we combined empirical features to identify subjective sentences in Portuguese and provide a comprehensive analysis of each set of features’ relative importance using a representative set of user reviews.
Keywords
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The Python package version was v3.0.0 with model pt_core_news_lg, except for the COMP and SUP POS tags, which were extracted with v2.2.0 and model pt_core_news_sm.
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Acknowledgments
The authors are thankful for the financial and material support provided by the Amazonas State Research Support Foundation (FAPEAM) through Project PPP 04/2017 and for the assistance of the Intelligent Systems Laboratory (LSI) of the Amazonas State University (UEA). We also gratefully acknowledge the support provided by the Gratificação de Produtividade Acadêmica (GPA) of the Amazonas State University (Portaria 086/2021).
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de Oliveira, M., de Melo, T. (2021). An Empirical Study of Text Features for Identifying Subjective Sentences in Portuguese. In: Britto, A., Valdivia Delgado, K. (eds) Intelligent Systems. BRACIS 2021. Lecture Notes in Computer Science(), vol 13074. Springer, Cham. https://doi.org/10.1007/978-3-030-91699-2_26
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