Abstract
This paper introduces graph-based aspect and rating classification, which utilizes multi-modal word co-occurrence network to solve aspect and sentiment classification tasks. Our model consists of three components: (1) word co-occurrence network construction, with aspect and sentiment labels as different modes; (2) dispersion computation for aspects and sentiments, and; (3) feedforward network for classification. Our experiment shows that proposed model outperforms baseline models, Word2Vec and LDA, in both aspect and sentiment classification tasks. Our classification model uses comparatively smaller vector size for representing words and sentences. The proposed model performs better in classifying out of vocabulary contexts.
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Jeon, S.W., Lee, H.J., Lee, H., Cho, S. (2019). Graph Based Aspect Extraction and Rating Classification of Customer Review Data. In: Li, G., Yang, J., Gama, J., Natwichai, J., Tong, Y. (eds) Database Systems for Advanced Applications. DASFAA 2019. Lecture Notes in Computer Science(), vol 11448. Springer, Cham. https://doi.org/10.1007/978-3-030-18590-9_13
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DOI: https://doi.org/10.1007/978-3-030-18590-9_13
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