Abstract
Opinion Mining (OM) of Internet reviews is one of the key issues in Natural Language Processing (NLP) field. This paper proposes a stacked Bi-LSTM aspect opinion extraction model in which semantic and syntactic features are both integrated. The model takes embedded vector which is composed by word embedding, POS tags and dependency relations as its input while taking label sequence as its output. The experimental results show the effectiveness of this structural features embedded stacked Bi-LSTM model on cross-domain and cross-language datasets, and indicate that this model outperforms the state-of-the-art methods.
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Han, Z., Jiang, X., Li, M., Zhang, M., Duan, D. (2018). An Integrated Semantic-Syntactic SBLSTM Model for Aspect Specific Opinion Extraction. In: Meng, X., Li, R., Wang, K., Niu, B., Wang, X., Zhao, G. (eds) Web Information Systems and Applications. WISA 2018. Lecture Notes in Computer Science(), vol 11242. Springer, Cham. https://doi.org/10.1007/978-3-030-02934-0_18
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DOI: https://doi.org/10.1007/978-3-030-02934-0_18
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