Sentiment classification based on multi-granularity computing and multi-criteria fusion

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Abstract The large amount of online user-generated content on the Web has created a need for unsupervised sentiment classification methods. Unsupervised sentiment classification methods based on sentiment words do not work well because the complex sentence structures and sentence types are seldom taken into account. Unsupervised sentiment classification methods based on self-learning have many errors when generating pseudo-labelled datasets. These limitations are reduced by the current method based on multi-granularity computing and multi-criteria fusion. The multi-granularity computing improves the accuracy of unsupervised sentiment classification methods based on sentiment words. The multi-criteria fusion reduces the number of errors in the pseudo-labelled data from the self-learning. Tests using three real Chinese review datasets show that the classification accuracy is 6.5% more accurate on average than with existing unsupervised sentiment classification methods.

Keywords sentiment classification unsupervised methods multi-granularity computing multi-criteria fusion

ZTFLH:

TP391.1

Issue Date: 15 May 2015

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	WANG Bingkun
	HUANG Yongfeng
	LI Xing

Cite this article:

WANG Bingkun,HUANG Yongfeng,LI Xing. Sentiment classification based on multi-granularity computing and multi-criteria fusion[J]. Journal of Tsinghua University(Science and Technology), 2015, 55(5): 497-502.

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http://jst.tsinghuajournals.com/EN/ OR http://jst.tsinghuajournals.com/EN/Y2015/V55/I5/497

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