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Data Analysis and Knowledge Discovery  2017, Vol. 1 Issue (7): 73-81    DOI: 10.11925/infotech.2096-3467.2017.0506
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Sentiment Analysis in Cross-Domain Environment with Deep Representative Learning
Chuanming Yu1,Bolin Feng1,Lu An2()
1 School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan 430073, China
2 School of Information Management, Wuhan University, Wuhan 430072, China
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Abstract  

[Objective] The study trains the model with the source domain of rich labeling/tagging data and to project the source and target domain documents into the same feature space. This paper tries to solve the performance issue facing the target domain due to the lack of data. [Methods] First, we collected the Chinese, English and Japanese comments on books, DVDs and music from Amazon. Then, we proposed a Cross Domain Deep Representation Model (CDDRM) based on the Convolutional Neural Network (CNN) and Structural Correspondence Learning (SCL) techniques. Finally, we conducted cross-domain knowledge transfer and sentiment analysis. [Results] We found the best F value of CDDRM was 0.7368, which indicated the effectiveness of the proposed model. [Limitations] The F1 value of our model on long articles needs to be improved. [Conclusions] Transfer learning could help supervised learning obtain good classification results with small training sets. Compared with traditional methods, CDDRM does not require the training and testing sets having same or similar data structure.

Key wordsCross Domain      Transfer Learning      Deep Representation Learning      Sentiment Analysis     
Received: 31 May 2017      Published: 26 July 2017

Cite this article:

Chuanming Yu,Bolin Feng,Lu An. Sentiment Analysis in Cross-Domain Environment with Deep Representative Learning. Data Analysis and Knowledge Discovery, 2017, 1(7): 73-81.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2017.0506     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2017/V1/I7/73

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