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

WANG Bingkun; HUANG Yongfeng; LI Xing

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Journal of Tsinghua University(Science and Technology) ›› 2015, Vol. 55 ›› Issue (5) : 497-502.

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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.

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

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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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