Dynamic commonsense knowledge fused method for Chinese implicit sentiment analysis |
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| Institution: | 1. School of Economics and Management, Beihang University, Beijing 100191, China;2. Key Laboratory of Complex System Analysis and Management Decision, Ministry of Education, Beijing 100191, China;1. School of Information Management, Nanjing University, Nanjing, China;2. School of Information Management, Wuhan University, Wuhan, China;3. Center for Studies of Information Resources, Wuhan University, Wuhan, China;4. School of Economics and Management, Nanjing University of Science and Technology, Nanjing, China;1. School of Management Engineering, Shandong Jianzhu University, Jinan, China;2. School of Business, University of Shanghai for Science and Technology, Shanghai, China;1. Cryptography and Cognitive Informatics Laboratory, AGH University of Science and Technology, 30 Mickiewicza Ave, Krakow 30-059, Poland;2. School of Computing, Engineering and Mathematical Sciences, La Trobe University, Melbourne, Australia;3. Department of Computer Science, Ryerson University, Canada |
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| Abstract: | Compared with explicit sentiment analysis that attracts considerable attention, implicit sentiment analysis is a more difficult task due to the lack of sentimental words. The abundant information in an external sentimental knowledge base can play a significant complementary and expansion role. In this paper, a sentimental commonsense knowledge graph embedded multi-polarity orthogonal attention model is proposed to learn the implication of the implicit sentiment. We analyzed the effectiveness of different knowledge relations in the ConceptNet knowledge base in detail, and proposed a matching and filtering method to distill useful knowledge tuples for implicit sentiment analysis automatically. By introducing the sentimental information in the knowledge base, the proposed model can extend the semantic of a sentence with an implicit sentiment. Then, a bi-directional long–short term memory model with multi-polarity orthogonal attention is adopted to fuse the distilled sentimental knowledge with the semantic embedding, effectively enriching the representation of sentences. Experiments on the SMP2019-ECISA implicit sentiment dataset show that our model fully utilizes the information of the knowledge base and improves the performance of Chinese implicit sentiment analysis. |
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| Keywords: | Chinese implicit sentiment analysis Sentimental knowledge Knowledge fusion Multi-polarity orthogonal attention |
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