Abstract
Targeting at the great commercial and social values of sentiment analysis of online product reviews, this paper puts forward an emotion-semantics enhanced multi-channel convolutional neural network (EMCCNN) approach to dig automatically out the emotional position of the reviewer. This EMCCNN model is featured firstly with a completed construction of emotional symbols datasets which are composed of Chinese emotion words, degree adverbs, negative words, common Internet slang and emoticons, secondly with three input channels containing a relatively independent text sequence and emotional symbol sequence in each, thirdly with weighted pooling which summarizes different channel’s convolution result with various weight, then joins with max pooling to form the final emotion vectors, which are subsequently classified with softmax function. The experiment result shows that, after the adjustment of super parameters, the F1 value of EMCCNN has been improved by 3.2% compared with the traditional CNN one.
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Ren, X., Fu, Y., Yang, X. (2023). Sentiment Classification of Chinese Commodity-Comment Based on EMCCNN Model. In: Hong, W., Weng, Y. (eds) Computer Science and Education. ICCSE 2022. Communications in Computer and Information Science, vol 1811. Springer, Singapore. https://doi.org/10.1007/978-981-99-2443-1_27
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DOI: https://doi.org/10.1007/978-981-99-2443-1_27
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