该文研究属性依赖情感知识学习。首先提出了一个新颖的话题模型,属性观点联合模型(Joint Aspect/Opinion model, JAO),来同时抽取评论实体属性及属性相关观点词信息。在此基础上,对于各个属性,构造属性依赖的词关系图,并在该图上应用马尔科夫随机行走过程来计算观点词到少量褒、贬种子词的游走时间(Hitting Time),进而估计这些词的属性依赖的情感极性分值。在餐馆点评数据上的实验表明所提出的方法能有效抽取属性相关观点词,同时有效估计其属性依赖的情感极性分值。
Abstract
This paper addresses the problem of learning aspect-dependent sentiment knowledge. Specifically, a novel topic model, called Joint Aspect/Opinion Model (JAO), is proposed to detect aspects and aspect-specific opinion words simultaneoasly in an unsupervised manner. Then, we propose to infer aspect-dependent sentiment polarity scores for these opinion words based on the hitting times from the words to a handful of positive/negative seed words, by applying Markov random walks over an aspect-specific word relation graph. Experimental results on restaurant review data show the effectiveness of the proposed approaches.
关键词
顾客点评 /
属性观点联合模型 /
游走时间 /
属性依赖情感知识
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Key words
online customer review /
joint aspect/opinion model /
hitting time /
aspect-dependent sentiment knowledge
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参考文献
[1] 黄萱菁, 张奇,吴苑斌. 文本情感倾向分析[J]. 中文信息学报,2011, 25(6):118-126.
[2] 姚天昉, 程希文, 徐飞玉, 等. 文本意见挖掘综述[J]. 中文信息学报, 2008, 22(5):71-80.
[3] 赵妍妍, 秦兵, 刘 挺. 文本情感分析[J]. 软件学报, 2010, 21(8):1834-1848.
[4] 周立柱,贺宇凯,王建勇.情感分析研究综述[J].计算机应用,2008,28(11):2725-2728.
[5] 吴琼, 谭松波, 程学旗.中文情感倾向性分析的相关研究进展[J]. 信息技术快报, 2010,8(4):16-38.
[6] 宋晓雷, 王素格, 李红霞.面向特定领域的产品评价对象自动识别研究[J]. 中文信息学报, 2010, 24(1): 89-93.
[7] 杨源, 马云龙, 林鸿飞. 评论挖掘中产品属性归类问题研究[J]. 中文信息学报,2012, 26(3):104-108.
[8] 徐琳宏, 林鸿飞, 赵晶. 情感语料库的构建和分[J]. 中文信息学报,2008, 22(1):116-122.
[9] 刘鸿宇, 赵妍妍, 秦兵, 等. 评价对象抽取及其倾向性分析[J]. 中文信息学报,2010, 24(1):84-88.
[10] 李寿山, 黄居仁. 基于Stacking组合分类方法的中文情感分类研究[J]. 中文信息学报,2010, 24(5):56-61.
[11] 谢丽星, 周明, 孙茂松. 基于层次结构的多策略中文微博情感分析和特征抽取[J]. 中文信息学报,2012, 26(1):73-83.
[12] Yue Lu, Malu Castellanos, Umeshwar Dayal, et al. Automatic Construction of a Context-Aware Sentiment Lexicon: An Optimization Approach[C]//Proceedings of WWW11, 2011.
[13] Wayne Xin Zhao, Jing Jiang, Hongfei Yan, et al. Jointly modeling aspects and opinions with a MaxEnt-LDA hybrid[C]//Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, 2010: 56-65.
[14] David M Blei, Andrew Y Ng, Michael I. Jordan. Latent Dirichlet allocation [J]. Journal of Machine Learning Research, 2003,3(3): 993-1022.
[15] Qiaozhu Mei, Dengyong Zhou, Kenneth Church. Query suggestion using hitting time[C]//Proceedings of the 17th ACM conference on Information and knowledge management (CIKM 08), 2008.
[16] Samuel Brody, Noemie Elhadad. An unsupervised aspect-sentiment model for online reviews[C]//Proceedings of Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics (HLT 10), 2010.
[17] Minqing Hu, Bing Liu. Mining and summarizing customer reviews[C]//Proceedings of SIGKDD,2004: 168-177.
[18] L Zhuang, F Jing, X Zhu. Movie review mining and summarization[C]//Proceedings of CIKM 06, 2006: 43-50.
[19] Thomas Hofmann. Probabilistic latent semantic indexing[C]//Proceedings of SIGIR 99, 1999: 50-57.
[20] I Titov, R McDonald. Modeling online reviews with multi-grain topic models[C]//Proceeding WWW 08,2008: 111-120.
[21] X Ling, Q Mei, C Zhai, et al. Mining multi-faceted overviews of arbitrary topics in a text collection[C]//Proceeding of the 14th ACM SIGKDD, 2008:497-505.
[22] A Hassan, D Radev. Identifying text polarity using random walks[C]//Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics (ACL 10), 2010:395-403.
[23] C Lin, Y He. Joint sentiment/topic model for sentiment analysis[C]//Proceedings of the 18th ACM conference on Information and knowledge management (CIKM 09), 2009: 375-384.
[24] Yohan Jo, Alice H. Oh. Aspect and sentiment unification model for online review analysis[C]//Proceedings of the fourth ACM international conference on Web search and data mining (WSDM 11).2011: 815-824.
[25] J Zhu, H Wang, B. K. Tsou, et al. Multi-aspect opinion polling from textual reviews[C]//Proceeding of CIKM 09, 2009: 1799-1802.
[26] Thomas L Griffiths, Mark Steyvers. Finding scientific topics [J]. Proceedings of the National Academy of Sciences,2004, 101(Suppl 1): 5228-5535.
[27] Gayatree Ganu, Noémie Elhadad, Amélie Marian. Beyond the stars: Improving rating predictions using review text content[C]//Proceedings of International Workshop on the Web and Databases, 2009.
[28] Ben He, Craig Macdonald, Jiyin He, et al. An effective statistical approach to blog post opinion retrieval[C]//Proceeding of CIKM 2008, 2008: 1063-1072.
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脚注
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基金
国家高技术研究发展计划(863计划)项目(2010AA012502、2010AA012503);国家自然科学基金资助项目(60933005、60903139、61100083)
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