标题:Document-level Sentiment Analysis Based on Domain-specific Sentiment Words
作者:Sun, Chengai ;Wang, Fang ;Tian, Gang
作者机构:[Sun, Chengai ;Wang, Fang ;Tian, Gang ] College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao; 266000, C 更多
会议名称:5th Annual International Conference on Network and Information Systems for Computers, ICNISC 2019
会议日期:19 April 2019 through 20 April 2019
来源:Journal of Physics: Conference Series
出版年:2019
卷:1288
期:1
DOI:10.1088/1742-6596/1288/1/012052
摘要:Aiming at the problem that the relationship between non-contiguous words in a sentence cannot be effectively captured, and the existing sentiment lexicon has poor adaptability in the field, a model for document-level sentiment analysis based on domain-specific sentiment words is constructed. We reconstructed word vectors using attention mechanism to capture the relationship between non-contiguous words in word vectors; Words are synthesized using Asymmetric Convolutional Neural Network. Sentences are synthesized by Bidirectional Gated Recurrent Neural Network based on attention mechanism to form document vector features; we used CNN to construct a domain-specific sentiment dictionary to generate emotional vector features; Document vector features and emotional vector features are combined using a linear binding layer to form document features that facilitate document classification. By comparing the performance of this method with other methods through experiments, the results show that there is a big advantage in classification accuracy, and can be widely used in various specific fields such as public health. © Published under licence by IOP Publishing Ltd.
收录类别:EI;SCOPUS
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85072126711&doi=10.1088%2f1742-6596%2f1288%2f1%2f012052&partnerID=40&md5=6211ebd65e295a48807ea75246a2efb7
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