Transcription of Attention-based LSTM for Aspect-level Sentiment Classification
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Attention-based LSTM for Aspect-level Sentiment Classification
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 606 615,Austin, Texas, November 1-5, 2016 Association for Computational LinguisticsAttention-based LSTM for Aspect-level Sentiment ClassificationYequan WangandMinlie HuangandLi Zhao*andXiaoyan ZhuState Key Laboratory on Intelligent Technology and SystemsTsinghua National Laboratory for Information Science and TechnologyDepartment of Computer Science and Technology, Tsinghua University, Beijing 100084, China*Microsoft Research Sentiment Classification is a fine-grained task in Sentiment analysis. Since itprovides more complete and in-depth results, Aspect-level Sentiment analysis has receivedmuch attention these years. In this paper, wereveal that the Sentiment polarity of a sentenceis not only determined by the content but isalso highly related to the concerned instance, The appetizers are ok, but theservice is slow.
sentiment lexicons (Rao and Ravichandran, 2009; Perez-Rosas et al., 2012; Kaji and Kitsuregawa, 2007),thelexicon-basedfeatureswerebuiltforsen-timent analysis (Mohammad et al., 2013). Most of these studies focus on building sentiment classiers with features, which include bag-of-words and sen-timent lexicons, using SVM (Mullen and Collier, 2004).
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