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Sentiment Analysis Method Based on Kmeans and Online Transfer Learning

Shengting Wu1, Yuling Liu1,*, Jingwen Wang2, Qi Li1

College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, China.
Department of Computer Science, University of Massachusetts Lowell, MA, USA.

* Corresponding Author: Yuling Liu. Email: email.

Computers, Materials & Continua 2019, 60(3), 1207-1222.


Sentiment analysis is a research hot spot in the field of natural language processing and content security. Traditional methods are often difficult to handle the problems of large difference in sample distribution and the data in the target domain is transmitted in a streaming fashion. This paper proposes a sentiment analysis method based on Kmeans and online transfer learning in the view of fact that most existing sentiment analysis methods are based on transfer learning and offline transfer learning. We first use the Kmeans clustering algorithm to process data from one or multiple source domains and select the data similar to target domain data to establish the classifier, so that the processed data does not negatively transfer the data in the target domain. And then create a new classifier based on the new target domain. The source domain classifier and target domain classifier are combined with certain weights by using the homogeneous online transfer learning method to achieve sentiment analysis. The experimental results show that this method has achieved better performance in terms of error rate and classification accuracy.


Cite This Article

APA Style
Wu, S., Liu, Y., Wang, J., Li, Q. (2019). Sentiment analysis method based on kmeans and online transfer learning. Computers, Materials & Continua, 60(3), 1207-1222.
Vancouver Style
Wu S, Liu Y, Wang J, Li Q. Sentiment analysis method based on kmeans and online transfer learning. Comput Mater Contin. 2019;60(3):1207-1222
IEEE Style
S. Wu, Y. Liu, J. Wang, and Q. Li "Sentiment Analysis Method Based on Kmeans and Online Transfer Learning," Comput. Mater. Contin., vol. 60, no. 3, pp. 1207-1222. 2019.


cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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