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.
王丙坤, 黄永峰, 李星. 基于多粒度计算和多准则融合的情感分类[J]. 清华大学学报（自然科学版）, 2015, 55(5): 497-502.
WANG Bingkun, HUANG Yongfeng, LI Xing. Sentiment classification based on multi-granularity computing and multi-criteria fusion. Journal of Tsinghua University(Science and Technology), 2015, 55(5): 497-502.
Pang B, Lee L L. Opinion mining and sentiment analysis [J]. Foundations and Trends in Information Retrieval, 2008, 2(1-2): 1-135.
LIU Bing. Sentiment analysis and opinion mining [J]. Synthesis Lectures on Human Language Technologies, 2012, 5(1): 1-167.
ZHANG Pu, HE Zhongshi. A weakly supervised approach to Chinese sentiment classification using partitioned self-training [J]. Journal of Information Science, 2013, 39(6): 815-831.
Pang B, Lee L L, Vaithyanathan S. Thumbs up? Sentiment classification using machine learning techniques [C]//Proceedings of Conference on Empirical Methods in Natural Language Processing. Philadelphia, USA: ACL, 2002: 79-86.
XIAO Min, GUO Yuhong. Feature space independent semi-supervised domain adaptation via kernel matching [J]. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 2015, 37(1): 54-66.
Pan S J, Ni X C, Sun J T, et al. Cross-domain sentiment classification via spectral feature alignment [C]//Proceedings of the 19th International Conference on World Wide Web. New York, NY, USA: ACM, 2010: 751-760.
LI Shoushan, WANG Zhongqing, ZHOU Guodong. Semi-supervised learning for imbalanced sentiment classification [C]//Proceedings of the Twenty-Second international joint conference on Artificial Intelligence. Barcelona, Spanish: AAAI, 2011: 1826-1831.
WAN Xiaojun. Bilingual co-training for sentiment classification of Chinese product reviews [J]. Computational Linguistics, 2011, 37(3): 587-616.
Turney P D. Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews [C]//The 40th annual meeting of the Association for Computational Linguistics. Philadelphia, USA: ACL, 2002: 417-424.
Ku L W, Lee L Y, Chen H H. Opinion extraction, summarization and tracking in news and blog corpora [C]//Proceedings ofAAAI-CAAW-06, the Spring Symposia on Computational Approaches to Analyzing Weblogs. Stanford, USA: AAAI, 2006.
Taboada M, Brooke J, Tofiloski M, et al. Lexicon-based methods for sentiment analysis [J]. Computational Linguistics, 2011, 37(2): 267-307.
TAN Songbo, WANG Yuefen, CHENG Xueqi. Combining learn-based and lexicon-based techniques for sentiment detection without using labeled examples [C]//Proceedings of the SIGIR. New York, NY, USA: ACM, 2008: 743-744.
WANG Bingkun, MIN Yulin, HUANG Yongfeng, et al. Chinese reviews sentiment classification based on quantified sentiment lexicon and fuzzy set [C]//2013 International Conference on Information Science and Technology. YangZhou, China: IEEE, 2013: 677-680.null