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Organizations and businesses: Opinion mining is equally, if not even more, important to businesses and organizations. For example, it is critical for a product manufacturer to know how consumers perceive its products and those of its competitors. This information is not only useful for marketing and product benchmarking but also useful for product design and product developments.
CROSS REFERENCES
Text mining
RECOMMENDED READING
1. Carenini, G., Ng, R. and Zwart, E. Extracting Knowledge from Evaluative Text. Proceedings of the Third International Conference on Knowledge Capture (K-CAP’05), 2005.
2. Dave, D., Lawrence, A., and Pennock, D. Mining the Peanut Gallery: Opinion Extraction and Semantic Classification of Product Reviews. Proceedings of International World Wide Web Conference (WWW’03), 2003.
3. Ding, X., Liu, B. and Yu, P. A Holistic Lexicon-Based Approach to Opinion Mining. Proceedings of the first ACM International Conference on Web search and Data Mining (WSDM’08), 2008.
4. Ganapathibhotla, G. and Liu, B. Identifying Preferred Entities in Comparative Sentences. To appear in Proceedings of the 22nd International Conference on Computational Linguistics (COLING’08),2008.
5. Hatzivassiloglou, V. and McKeown, K. Predicting the Semantic Orientation of Adjectives. ACL-EACL’97, 1997.
6. Hu, M and Liu, B. Mining and Summarizing Customer Reviews. Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’04), 2004.
7. Jindal, N. and Liu, B. Mining Comparative Sentences and Relations. Proceedings of National Conference on Artificial Intelligence (AAAI’06), 2006.
8. Kanayama, H. and Nasukawa, T. Fully Automatic Lexicon Expansion for Domain-Oriented Sentiment Analysis. Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing (EMNLP’06), 2006.
9. Kim, S. and Hovy, E. Determining the Sentiment of Opinions. Proceedings of the 20th International
Conference on Computational Linguistics (COLING’04), 2004.
10. Liu, B., Hu, M. and Cheng, J. Opinion Observer: Analyzing and Comparing Opinions on the Web. Proceedings of International World Wide Web Conference (WWW’05), 2005.
11. Pang, B., Lee, L. and Vaithyanathan, S. Thumbs up? Sentiment Classification Using Machine Learning Techniques. Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing (EMNLP’02), 2002.
12. Popescu, A.-M. and Etzioni, O. Extracting Product Features and Opinions from Reviews. Proceedings of the 2005 Conference on Empirical Methods in Natural Language Processing (EMNLP’05), 2005.
13. Turney, P. Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews. ACL’02, 2002.
14. Wiebe, J. and Riloff, E. Creating Subjective and Objective Sentence Classifiers from Unannotated Texts. Proceedings of International Conference on Intelligent Text Processing and Computational Linguistics (CICLing’05), 2005.
15. Wilson, T., Wiebe, J. and Hwa, R. Just How Mad Are You? Finding Strong and Weak Opinion Clauses. Proceedings of National Conference on Artificial Intelligence (AAAI’04), 2004.
B.原文的翻译
观点挖掘同义词情感分析
定义假如有一组评价文本D，D含有关于一个对象的评论的观点或者情感，情感挖掘的目的是抽取出在文档d中（d属于D）被评论的对象的属性和组成，和确定这些评论是正向的，负向的还是中性的。论文网
历史背景
世界上的文本信息可以被广泛的分为两个大类：事实和观点。事实是对世界上的实体或者事件的客观描述，观点是主观描述，这些描述反映了人们对实体和事件的情感和感受。大多数关于文本信息已经存在的研究已经被独有的发现在事实信息的挖掘和检索上，比如：信息检索，网页搜寻和许多其他的文本挖掘和自然语言处理任务。几乎没有现成的工作是对观点的直到最近才有。但是，观点是很重要的以至于无论什么时候一个人想要做出决定，他就想要听取别人的观点。这不仅仅适合个体也适合于组织。