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Cross-Domain Text Sentiment Analysis Based on CNN_FT Method

School of Computer Science & Engineering, Dalian Minzu University, Dalian 116600, China
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Information 2019, 10(5), 162; https://doi.org/10.3390/info10050162
Received: 25 February 2019 / Revised: 13 April 2019 / Accepted: 22 April 2019 / Published: 1 May 2019
(This article belongs to the Section Information Processes)
Transfer learning is one of the popular methods for solving the problem that the models built on the source domain cannot be directly applied to the target domain in the cross-domain sentiment classification. This paper proposes a transfer learning method based on the multi-layer convolutional neural network (CNN). Interestingly, we construct a convolutional neural network model to extract features from the source domain and share the weights in the convolutional layer and the pooling layer between the source and target domain samples. Next, we fine-tune the weights in the last layer, named the fully connected layer, and transfer the models from the source domain to the target domain. Comparing with the classical transfer learning methods, the method proposed in this paper does not need to retrain the network for the target domain. The experimental evaluation of the cross-domain data set shows that the proposed method achieves a relatively good performance. View Full-Text
Keywords: cross-domain; sentiment classification; transfer learning; convolutional neural network; word2vec cross-domain; sentiment classification; transfer learning; convolutional neural network; word2vec
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Meng, J.; Long, Y.; Yu, Y.; Zhao, D.; Liu, S. Cross-Domain Text Sentiment Analysis Based on CNN_FT Method. Information 2019, 10, 162.

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