Sentiment mining of commodity reputation data based on joint model

doi:10.16511/j.cnki.qhdxxb.2017.26.042

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Abstract This paper presents a probabilistic graphical model to simultaneously extract objects, aspects and sentiments from commodity reputation data. The underlying assumption is that the aspect distribution depends on the object distribution, while the sentiment distribution depends on the aspect distribution. The model further assumes that words are the smallest sampling units and is fully unsupervised. Tests on car reputation data show that this model can predict the aspect and sentiment categories of commodity reviews and can simultaneously extract object information from the reviews.

Keywords commodity reputation data joint model sentiment mining unsupervised learning

ZTFLH:

TP391.1

Issue Date: 15 September 2017

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	WANG Suge
	LI Dayu
	LI Yang

Cite this article:

WANG Suge,LI Dayu,LI Yang. Sentiment mining of commodity reputation data based on joint model[J]. Journal of Tsinghua University(Science and Technology), 2017, 57(9): 926-931.

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http://jst.tsinghuajournals.com/EN/10.16511/j.cnki.qhdxxb.2017.26.042 OR http://jst.tsinghuajournals.com/EN/Y2017/V57/I9/926

[1]	Blei D M, Ng A Y, Jordan M I. Latent Dirichlet allocation[J]. Journal of Machine Learning Research, 2003, 3:993-1022.
[2]	Lin C, He Y. Joint sentiment/topic model for sentiment analysis[C]//Proceedings of the 18th ACM Conference on Information and Knowledge Management. New York, NY, USA:ACM, 2009:375-384.
[3]	He Y, Lin C, Alani H. Automatically extracting polarity-bearing topics for cross-domain sentiment classification[C]//Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics:Human Language Technologies-Volume 1. Stroudsburg, PA, USA:Association for Computational Linguistics, 2011:123-131.
[4]	Lin C, He Y, Everson R, et al. Weakly supervised joint sentiment-topic detection from text[J].IEEE Transactions on Knowledge and Data Engineering, 2012, 24(6):1134-1145.
[5]	Jo Y, Oh A H. Aspect and sentiment unification model for online review analysis[C]//Proceedings of the 4th ACM International Conference on Web Search and Data Mining. New York, NY, USA:ACM, 2011:815-824.
[6]	孙艳, 周学广, 付伟. 基于主题情感混合模型的无监督文本情感分析[J]. 北京大学学报(自然科学版), 2013, 49(1):102-108.SUN Yan, ZHOU Xueguang, FU Wei. Unsupervised topic and sentiment unification model for sentiment analysis[J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2013, 49(1):102-108. (in Chinese)
[7]	Li F, Huang M, Zhu X. Sentiment analysis with global topics and local dependency[C]//Twenty-Fourth AAAI Conference on Artificial Intelligence. Hamilton, Canada:AAAI, 2010:1371-1376.
[8]	Moghaddam S, Ester M. On the design of LDA models for aspect-based opinion mining[C]//Proceedings of the 21st ACM International Conference on Information and Knowledge Management. New York, NY, USA:ACM, 2012:803-812.
[9]	Titov I, McDonald R T. A joint model of text and aspect ratings for sentiment summarization[C]//The 46th Annual Meeting of the Association for Computational Linguistics:Human Language Technologies. Stroudsburg, PA, USA:Association for Computational Linguistics, 2008:308-316.
[10]	Titov I, McDonald R. Modeling online reviews with multi-grain topic models[C]//Proceedings of the 17th International Conference on World Wide Web. New York, NY, USA:ACM, 2008:111-120.
[11]	Chen Z, Mukherjee A, Liu B. Aspect extraction with automated prior knowledge learning[C]//Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics. Stroudsburg, PA, USA:Association for Computational Linguistics, 2014:347-358.
[12]	Wu Y, Ester M. FLAME:A probabilistic model combining aspect based opinion mining and collaborative filtering[C]//Proceedings of the Eighth ACM International Conference on Web Search and Data Mining. New York, NY, USA:ACM, 2015:199-208.
[13]	Mcauley J, Pandey R, Leskovec J. Inferring networks of substitutable and complementary products[C]//Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA:ACM, 2015:785-794.
[14]	Walsh B. Markov chain Monte Carlo and Gibbs sampling[J]. Notes, 2004, 91(8):497-537.
[15]	冯淑芳, 王素格. 面向观点挖掘的汽车评价本体知识库的构建[J]. 计算机应用与软件, 2011, 28(5):45-47.FENG Shufang, WANG Suge. Automobile reviews ontology knowledge base construction oriented towards opinion mining[J]. Computer Applications & Software, 2011, 28(5):45-47. (in Chinese) url: http://dx.doi.org/ter Applications