Microblog Sentiment Analysis Method Based on Spectral Clustering


Shi Dong, Xingang Zhang, Ya Li, Journal of Information Processing Systems Vol. 14, No. 3, pp. 727-739, Jun. 2018  

10.3745/JIPS.04.0076
Keywords: Machine Learning, RDM, Sentiment Analysis, Spectral Cluster
Fulltext:

Abstract

This study evaluates the viewpoints of user focus incidents using microblog sentiment analysis, which has been actively researched in academia. Most existing works have adopted traditional supervised machine learning methods to analyze emotions in microblogs; however, these approaches may not be suitable in Chinese due to linguistic differences. This paper proposes a new microblog sentiment analysis method that mines associated microblog emotions based on a popular microblog through user-building combined with spectral clustering to analyze microblog content. Experimental results for a public microblog benchmark corpus show that the proposed method can improve identification accuracy and save manually labeled time compared to existing methods.


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Cite this article
[APA Style]
Shi Dong, Xingang Zhang, & Ya Li (2018). Microblog Sentiment Analysis Method Based on Spectral Clustering. Journal of Information Processing Systems, 14(3), 727-739. DOI: 10.3745/JIPS.04.0076.

[IEEE Style]
S. Dong, X. Zhang and Y. Li, "Microblog Sentiment Analysis Method Based on Spectral Clustering," Journal of Information Processing Systems, vol. 14, no. 3, pp. 727-739, 2018. DOI: 10.3745/JIPS.04.0076.

[ACM Style]
Shi Dong, Xingang Zhang, and Ya Li. 2018. Microblog Sentiment Analysis Method Based on Spectral Clustering. Journal of Information Processing Systems, 14, 3, (2018), 727-739. DOI: 10.3745/JIPS.04.0076.