This paper first analyzes the limitation of the existing methods of aspect identification. Then a novel method is presented which utilizes Self-organization map to identify the aspects from product reviews. A new SOM display named “Attribute Accumulative Matrix” is defined. In order to verify the validity of the method, we extract the product aspects from the restaurant reviews on a website. The experiment results show that this approach can effectively extract the product aspects.
余传明. 从用户评论中挖掘产品属性——基于SOM的实现[J]. 现代图书情报技术, 2009, 25(5): 61-66.
Yu Chuanming. Mining Product Aspects from User Reviews----An SOM-based Approach. New Technology of Library and Information Service, 2009, 25(5): 61-66.
[1] Somasundaran S, Ruppenhofer J, Wiebe J. Detecting Arguing and Sentiment in Meetings[C]. In: Proceedings of Workshop on Discourse and Dialogue(SIGdial’2007), Antwerp, Belgium, September 2007:311-319.
[2] Yang C, Lin K, and Chen H H. Emotion Classification Using Web Blog Corpora[C]. In: Proceedings of the 2007 IEEE/WIC/ACM International Conference on Web Intelligence(WI-IAT’2007), Silicon Valley, U.S.A. 2007:275-279.
[3] Fung G P, Yu J X,Lu H. The Predicting Power of Textual Information on Financial Markets[J].IEEE Intelligent Informatics Bulletin,2005,5(1):1-10.
[4] Zhuang L, et al. Movie Review Mining and Summarization[C].In: Proceedings of ACM International Conference on Information and Knowledge Management(CIKM’2006), Arlington, Virginia, U.S.A. 2006:1-7.
[5] Hu M, Liu B. Mining and Summarizing Customer Reviews[C]. In: Proceeding of the 10th Knowledge Discovery and Data Mining Conference(KDD’2004), Seattle, WA, U.S.A. 2004:168-177.
[6] Kim S. M, et al. Determining the Sentiment of Opinions[C].In: Proceedings of the 20th International Conference on Computational Linguistics,Geneva, Switzerland. 2004:1-8.
[7] Popescu A M,Etzioni O. Extracting Product Features and Opinions from Reviews[C]. In: Proceedings of Empirical Methods in Natural Language Processing(EMNLP’2005) ,Vancouver, B.C., Canada.2005:1-8.
[8] Hofmann T. Probabilistic Latent Semantic Indexing[C]. In: Proceedings of the 22nd Annual International SIGIR Conference on Research and Development in Information Retrieval, California, U.S.A. 1999: 1-8.
[9] Blei D M, Ng A Y, Jordan M I. Latent Dirichlet Allocation[J]. Journal of Machine Learning Research, 2003(3):993-1022.
[10] Blei D. and Lafferty J. Correlated Topic Models[C]. In: Proceeding of the 20th Annual Conference on Neural Information Processing Systems, Vancouver, B.C., Canada.2006:1-8.
[11] Kohonen T. Self-Organized Formation of Topologically Correct Teaturc Maps[J]. Biological Cybernetics, 1982,43(1):59-69.
[12] Ultsch A. Maps for the Visualization of High-dimensional Data Spaces[C]. In: Proceedings of Workshop on Self-Organizing Maps (WSOM’2003), Hibikino, Kitakyushu, Japan. 2003:225-230.
[13] 刘群等. 基于层叠隐马模型的汉语词法分析[J].计算机研究与发展,2004(8):1421-1430.
[14] About SOm Toolbox[EB/OL]. [2008-10-16]http://www.cis.hut.fi/projects/somtoolbox/about.
[15] SOM Toolbox[EB/OL].[2008-10-16]http://www.cis.hut.fi/somtoolbox/package/docs2/som_norm_ variable.html.
[16] Kohonen T. Self-Organizing Maps[M] (3rd ed.). Berlin: Springer, 2001.
[17] Stijn V L, Bert V C, Jeroen M, Bart W,et al. Prediction of Dose Escalation for Rheumatoid Arthritis Patients under Infliximab Treatment [J]. Engineering Applications of Artificial Intelligence, 2006, 19(7): 819-828.
[18] Kohonen T. Things you haven’t heard about the Self-Organizing Map[C]. In:Proceedings of International Conference on Neural Networks (ICNN’ 1993), San Francisco, U.S.A. 1993:1147-1156.
[19] Goldensohn S B,Hannan K, McDonald R, et al. Building a Sentiment Summarizer for Local Service Reviews[C].In: Proceedings of NLP Challenges in the Information Explosion Era (NLPIX’2008),Beijing, China. 2008:1-9.