Abstract
Feature-based opinion mining and summarizing (FOMS) of reviews is an interesting issue in the opinion mining field. SentiWordNet is an useful lexical resource for opinion mining, especially for FOMS. In this paper, an upgrading FOMS model on Vietnamese reviews on mobile phone products is described. Feature words and opinion words were extracted based on some Vietnamese syntactic rules. Extracted feature words were grouped by using HAC clustering and semi-supervised SVM-kNN classification. Customers’ opinion orientation and summarization on features was determined by using a VietSentiWordNet, which had been extended from an initial VietSentiWordNet. Experiments on feature extraction and opinion summarization on features are showed.
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Ha, QT., Vu, TT., Pham, HT., Luu, CT. (2011). An Upgrading Feature-Based Opinion Mining Model on Vietnamese Product Reviews. In: Zhong, N., Callaghan, V., Ghorbani, A.A., Hu, B. (eds) Active Media Technology. AMT 2011. Lecture Notes in Computer Science, vol 6890. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23620-4_21
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DOI: https://doi.org/10.1007/978-3-642-23620-4_21
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