Abstract
At present, convolutional neural network has achieved good results in text emotional classification, but in several common models, it does not make use of a large number of prior knowledge that human society has now acquired. This paper proposes a new CNN model based on emotional dictionary: emotional knowledge-CNN (EK-CNN), which uses emotional dictionary as additional knowledge to improve the performance of the model in emotional classification. The model has been validated on three real data sets, and is superior to the existing technology model for text emotion classification.
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Acknowledgements
We would like to thank the anonymous reviewers for their valuable comments and suggestions. This work is supported by Shandong Provincial Natural Science Foundation of China under Grant ZR2018MF009, ZR2019MF003, The State Key Research Development Program of China under Grant 2017YFC0804406, National Natural Science Foundation of China under Grant 91746104, the Special Funds of Taishan Scholars Construction Project, and Leading Talent Project of Shandong University of Science and Technology.
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Mao, GH., Fan, JC., Zhang, YM. (2021). Convolutional Neural Network Combined with Emotional Dictionary Apply in Chinese Text Emotional Classification. In: Balas, V.E., Pan, JS., Wu, TY. (eds) Advances in Smart Vehicular Technology, Transportation, Communication and Applications. Smart Innovation, Systems and Technologies, vol 226. Springer, Singapore. https://doi.org/10.1007/978-981-16-1209-1_9
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DOI: https://doi.org/10.1007/978-981-16-1209-1_9
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