Abstract
Automatic aspect identification and clustering are critical tasks for opinion mining/sentiment analysis, as users employ varied terms (explicitly or not) to evaluate objects of interest and their characteristics. In this paper, we focus on aspect clustering methods and present a new approach to group implicit and explicit aspects from online reviews. We evaluate four linguistic methods inspired in the literature and one statistical method (using word embeddings), and also propose a new one, based on varied linguistic knowledge. We test the methods in three commonly used domains and show that the method that we propose significantly outperforms the other methods by a large margin.
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We only look for coreference, foreignism and diminutive-augmentative relations, because we empirically observed that they were the most accurate ones in this step.
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The authors are grateful to FAPESP, CAPES and CNPq for supporting this work.
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Vargas, F.A., Pardo, T.A.S. (2018). Aspect Clustering Methods for Sentiment Analysis. In: Villavicencio, A., et al. Computational Processing of the Portuguese Language. PROPOR 2018. Lecture Notes in Computer Science(), vol 11122. Springer, Cham. https://doi.org/10.1007/978-3-319-99722-3_37
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