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Aspect and Entity Extraction for Opinion Mining

  • Lei Zhang
  • Bing Liu
Part of the Studies in Big Data book series (SBD, volume 1)

Abstract

Opinion mining or sentiment analysis is the computational study of people’s opinions, appraisals, attitudes, and emotions toward entities such as products, services, organizations, individuals, events, and their different aspects. It has been an active research area in natural language processing and Web mining in recent years. Researchers have studied opinion mining at the document, sentence and aspect levels. Aspect-level (called aspect-based opinion mining) is often desired in practical applications as it provides the detailed opinions or sentiments about different aspects of entities and entities themselves, which are usually required for action. Aspect extraction and entity extraction are thus two core tasks of aspect-based opinion mining. In this chapter, we provide a broad overview of the tasks and the current state-of-the-art extraction techniques.

Keywords

Noun Phrase Natural Language Processing Opinion Mining Latent Dirichlet Allocation Sentiment Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Bethard, S., Yu, H., Thornton, A., Hatzivassiloglou, V., Jurafsky, D.: Automatic extraction of opinion propositions and their holders. In: Proceedings of the AAAI Spring Symposium on Exploring Attitude and Affect in Text (2004)Google Scholar
  2. Blair-Goldensohn, S., Hannan, K., McDonald, R., Neylon, T., Reis, G.A., Reyna, J.: Building a sentiment summarizer for local service reviews. In: Proceedings of International Conference on World Wide Web Workshop of NLPIX, WWW-NLPIX-2008 (2008)Google Scholar
  3. Blei, D., Ng, A., Jordan, M.: Latent dirichlet allocation. The Journal of Machine Learning Research (2003)Google Scholar
  4. Bloom, K., Grag, N., Argamon, S.: Extracting apprasial expressions. In: Proceedings of the 2007 Annual Conference of the North American Chapter of the ACL (NAACL 2007) (2007)Google Scholar
  5. Branavan, S.R.K., Chen, H., Eisenstein, J., Barzilay, R.: Learning document-level semantic properties from free-text annotations. In: Proceedings of Annual Meeting of the Association for Computational Linguistics, ACL 2008 (2008)Google Scholar
  6. Brown, F.P., Della Pietra, S.A., Della Pietra, V.J., Mercer, R.L.: The mathematics of statitical machine translation: parameter estimation. Computational Linguistics (1993)Google Scholar
  7. Brody, S., Elhadad, S.: An unsupervised aspect-sentiment model for online reviews. In: Proceedings of the 2010 Annual Conference of the North American Chapter of the ACL, NAACL 2010 (2010)Google Scholar
  8. Carenini, G., Ng, R., Pauls, A.: Multi-Document summarization of evaluative text. In: Proceeding of Conference of the European Chapter of the ACL, EACL 2006 (2006)Google Scholar
  9. Carenini, G., Ng, R., Zwart, E.: Extracting knowledge from evaluative text. In: Proceedings of Third International Conference on Knowledge Capture, K-CAP 2005 (2005)Google Scholar
  10. Choi, Y., Cardie, C., Riloff, E., Patwardhan, S.: Identifying sources of opinions with conditional random fields and extraction patterns. In: Proceedings of the Human Language Technology Conference and the Conference on Empirical Methods in Natural Language Processing, HLT/EMNLP 2005 (2005)Google Scholar
  11. Dempster, P., Laird, A.M.N., Rubin, B.D.: Maximum likelihood from incomplete data via the EM algorithms. Journal of the Royal Statistical Society, Series B (1977)Google Scholar
  12. Fei, G., Liu, B., Hsu, M., Castellanos, M., Ghosh, R.: A dictionary-based approach to identifying aspects implied by adjectives for opinion mining. In: Proceedings of International Conference on Computational Linguistics, COLING 2012 (2012)Google Scholar
  13. Ghahramani, Z., Heller, K.A.: Bayesian sets. In: Proceeding of Annual Neural Information Processing Systems, NIPS 2005 (2005)Google Scholar
  14. Ghani, R., Probst, K., Liu, Y., Krema, M., Fano, A.: Text mining for product attribute extraction. ACM SIGKDD Explorations Newsletter 8(1) (2006)Google Scholar
  15. Gilks, R.W., Richardson, S., Spiegelhalter, D.: Markov Chain Monte Carlo in practice. Chapman and Hall (1996)Google Scholar
  16. Grosz, J.B., Winstein, S., Joshi, A.K.: Centering: a framework for modeling the local coherence of discourse. Computational Linguistics 21(2) (1995)Google Scholar
  17. Guo, H., Zhu, H., Guo, Z., Zhang, X., Su, Z.: Product feature categorization with multilevel latent semantic association. In: Proceedings of ACM International Conference on Information and Knowledge Management, CIKM 2009 (2009)Google Scholar
  18. Hai, Z., Chang, K., Kim, J.: Implicit feature identification via co-occurrence association rule mining. Computational Linguistic and Intelligent Text Processing (2011)Google Scholar
  19. Hai, Z., Chang, K., Cong, G.: One seed to find them all: mining opinion features via association. In: Proceedings of ACM International Conference on Information and Knowledge Management, CIKM 2012 (2012)Google Scholar
  20. Hofmann, T.: Unsupervised learning by probabilistic latent semantic analysis. Machine Learning (2001)Google Scholar
  21. Hu, M., Liu, B.: Mining opinion features in customer reviews. In: Proceedings of National Conference on Artificial Intelligence, AAAI 2004 (2004a)Google Scholar
  22. Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2004 (2004b)Google Scholar
  23. Jakob, N., Gurevych, I.: Extracting opinion targets in a single and cross-domain setting with conditional random fields. In: Proceedings of Conference on Empirical Methods in Natural Language Processing, EMNLP 2010 (2010)Google Scholar
  24. Jin, W., Ho, H.: A novel lexicalized HMM-based learning framework for web opinion mining. In: Proceedings of International Conference on Machine Learning, ICML 2009 (2009a)Google Scholar
  25. Jin, W., Ho, H., Srihari, R.K.: OpinionMiner: a novel machine learn-ing system for web opinion mining and extraction. In: Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2009 (2009b)Google Scholar
  26. Jindal, N., Liu, B.: Mining comparative sentences and relations. In: Proceedings of National Conference on Artificial Intelligence, AAAI 2006 (2006a)Google Scholar
  27. Jindal, N., Liu, B.: Identifying comparative sentences in text documents. In: Proceedings of ACM SIGIR International Conference on Information Retrieval, SIGIR 2006 (2006b)Google Scholar
  28. Jo, Y., Oh, A.: Aspect and sentiment unification model for online review analysis. In: Proceedings of the Conference on Web Search and Web Data Mining, WSDM 2011 (2011)Google Scholar
  29. Kessler, J., Nicolov, N.: Targeting sentiment expressions through supervised ranking of linguistic configurations. In: Proceedings of the International AAAI Conference on Weblogs and Social Media, ICWSM 2009 (2009)Google Scholar
  30. Kim, S.M., Hovy, E.: Extracting opinions, opinion holders, and topics expressed in online news media text. In: Proceedings of the ACL Workshop on Sentiment and Subjectivity in Text (2006)Google Scholar
  31. Kleinberg, J.: Authoritative sources in hyper-linked environment. Journal of the ACM 46(5), 604–632 (1999)MathSciNetCrossRefzbMATHGoogle Scholar
  32. Kobayashi, N., Inui, K., Matsumoto, Y.: Extracting aspect-evaluation and aspect-of relations in opinion mining. In: Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, EMNLP 2007 (2007)Google Scholar
  33. Ku, L., Liang, Y., Chen, H.: Opinion extraction, summarization and tracking in news and blog corpora. In: Proceedings of AAAI-CAAW 2006 (2006)Google Scholar
  34. Lafferty, J., McCallum, A., Pereira, F.: Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: Proceedings of International Conference on Machine Learning, ICML 2001 (2001)Google Scholar
  35. Lee, L.: Measures of distributional similarity. In: Proceedings of Annual Meeting of the Association for Computational Linguistics, ACL 1999 (1999)Google Scholar
  36. Li, F., Han, C., Huang, M., Zhu, X., Xia, Y., Zhang, S., Yu, H.: Structure-aware review mining and summarization. In: Proceedings of International Conference on Computational Linguistics, COLING 2010 (2010a)Google Scholar
  37. Li, F., Pan, S.J., Jin, Q., Yang, Q., Zhu, X.: Cross-Domain co-extraction of sentiment and topic lexicons. In: Proceedings of Annual Meeting of the Association for Computational Linguistics, ACL 2012 (2012a)Google Scholar
  38. Li, S., Wang, R., Zhou, G.: Opinion target extraction using a shallow semantic parsing framework. In: Proceedings of National Conference on Artificial Intelligence, AAAI 2012 (2012b)Google Scholar
  39. Li, X., Liu, B.: Learning to classify texts using positive and unlabeled data. In: Proceedings of International Joint Conferences on Artificial Intelligence, IJCAI 2003 (2003)Google Scholar
  40. Li, X., Liu, B., Ng, S.: Learning to identify unexpected instances in the test set. In: Proceedings of International Joint Conferences on Artificial Intelligence, IJCAI 2007 (2007)Google Scholar
  41. Li, X., Zhang, L., Liu, B., Ng, S.: Distributional similarity vs. PU learning for entity set expansion. In: Proceedings of Annual Meeting of the Association for Computational Linguistics, ACL 2010 (2010b)Google Scholar
  42. Lin, C., He, Y.: Joint sentiment/topic model for sentiment analysis. In: Proceedings of ACM International Conference on Information and Knowledge Management, CIKM 2009 (2009)Google Scholar
  43. Lin, D.: Dependency-based evaluation of MINIPAR. In: Proceedings of the Workshop on Evaluation of Parsing System, ICLRE 1998 (1998)Google Scholar
  44. Liu, B.: Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data, 1st edn. Springer (2006), 2nd edn. (2011)Google Scholar
  45. Liu, B.: Sentiment analysis and subjectivity, 2nd edn. Handbook of Natural Language Processing (2010)Google Scholar
  46. Liu, B.: Sentiment Analysis and Opinion Mining. Morgan & Claypool Publishers (2012)Google Scholar
  47. Liu, B., Hu, M., Cheng, J.: Opinion observer: analyzing and comparing opinions on the web. In: Proceedings of International Conference on World Wide Web, WWW 2005 (2005)Google Scholar
  48. Liu, B., Lee, W.-S., Yu, P.S., Li, X.: Partially supervised text classification. In: Proceedings of International Conference on Machine Learning, ICML 2002 (2002)Google Scholar
  49. Liu, K., Xu, L., Zhao, J.: Opinion target extraction using word-based translation model. In: Proceeding of Conference on Empirical Methods in Natural Language Processing, EMNLP 2012 (2012)Google Scholar
  50. Long, C., Zhang, J., Zhu, X.: A review selection approach for accurate feature rating estimation. In: Proceedings of International Conference on Computational Linguistics, COLING 2010 (2010)Google Scholar
  51. Lu, Y., Duan, H., Wang, H., Zhai, C.: Exploiting structured ontology to organize scattered online opinions. In: Proceedings of International Conference on Computational Linguistics, COLING 2010 (2010)Google Scholar
  52. Lu, Y., Zhai, C., Sundaresan, N.: Rated aspect summarization of short comments. In: Proceedings of International Conference on World Wide Web, WWW 2009 (2009)Google Scholar
  53. Ma, T., Wan, X.: Opinion target extraction in Chinese news comments. In: Proceedings of International Conference on Computational Linguistics (COLING 2010) (2010)Google Scholar
  54. Mauge, K., Rohanimanesh, K., Ruvini, J.D.: Structuring e-commerce inventory. In: Proceedings of Annual Meeting of the Association for Computational Linguistics, ACL 2012 (2012)Google Scholar
  55. Mukherjee, A., Liu, B.: Aspect extraction through semi-supervised modeling. In: Proceedings of Annual Meeting of the Association for Computational Linguistics, ACL 2012 (2012)Google Scholar
  56. Mei, Q., Ling, X., Wondra, M., Su, H., Zhai, C.: Topic sentiment mixture: modeling facets and opinions in weblogs. In: Proceedings of International Conference on World Wide Web, WWW 2007 (2007)Google Scholar
  57. Moghaddam, S., Ester, M.: Opinion digger: an unsupervised opinion miner from unstructured product reviews. In: Proceedings of ACM International Conference on Information and Knowledge Management, CIKM 2010 (2010)Google Scholar
  58. Moghaddam, S., Ester, M.: ILDA: interdependent LDA model for learning latent aspects and their ratings from online product reviews. In: Proceedings of ACM SIGIR International Conference on Information Retrieval, SIGIR 2011 (2011)Google Scholar
  59. Neter, J., Wasserman, W., Whitmore, G.A.: Applied Statistics. Allyn and Bacon (1993) Google Scholar
  60. Pang, B., Lee, L.: Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval 2(1-2), 1–135 (2008)CrossRefGoogle Scholar
  61. Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up?: sentiment classification using machine learning techniques. In: Proceedings of Conference on Empirical Methods in Natural Language Processing, EMNLP 2002 (2002)Google Scholar
  62. Pantel, P., Crestan, E., Borkovsky, A., Popescu, A.: Web-Scale distributional similarity and entity set expansion. In: Proceedings of the 2009 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, EMNLP 2009 (2009)Google Scholar
  63. Popescu, A., Etzioni, O.: Extracting product features and opinions from reviews. In: Proceedings of Conference on Empirical Methods in Natural Language Processing, EMNLP 2005 (2005)Google Scholar
  64. Putthividhya, D., Hu, J.: Bootstrapped name entity recognition for product attribute extraction. In: Proceedings of Conference on Empirical Methods in Natural Language Processing, EMNLP 2011 (2011)Google Scholar
  65. Qiu, G., Liu, B., Bu, J., Chen, C.: Opinion word expansion and target extraction through double propagation. Computational Linguistics (2011)Google Scholar
  66. Rabiner, R.L.: A tutorial on hidden markov models and selected applications in speech recognition. Proceedings of IEEE 77(2) (1989)Google Scholar
  67. Sarawagi, S.: Information Extraction. Foundations and Trends in Databases (2008)Google Scholar
  68. Sauper, C., Haghighi, A., Barzilay, R.: Content models with attribute. In: Proceedings of Annual Meeting of the Association for Computational Linguistics, ACL 2011 (2011)Google Scholar
  69. Scaffidi, C., Bierhoff, K., Chang, E., Felker, M., Ng, H., Jin, C.: Red opal: product-feature scoring from reviews. In: Proceedings of the 9th International Conference on Electronic Commerce, EC 2007 (2007)Google Scholar
  70. Stoyanov, V., Cardie, C.: Topic identification for fine-grained opinion analysis. In: Proceedings of International Conference on Computational Linguistics, COLING 2008 (2008)Google Scholar
  71. Su, Q., Xu, X., Guo, H., Guo, Z., Wu, X., Zhang, X., Swen, B., Su, Z.: Hidden sentiment association in Chinese web opinion mining. In: Proceedings of International Conference on World Wide Web, WWW 2008 (2008)Google Scholar
  72. Sutton, C., McCallum, A.: An introduction to conditional random fields for relational learning. Introduction to Statistical Relational Learning. MIT Press (2006)Google Scholar
  73. Titov, I., McDonald, R.: Modeling online reviews with multi-grain topic models. In: Proceedings of International Conference on World Wide Web, WWW 2008 (2008a)Google Scholar
  74. Titov, I., McDonald, R.: A joint model of text and aspect ratings for sentiment summarization. In: Proceedings of Annual Meeting of the Association for Computational Linguistics, ACL 2008 (2008b)Google Scholar
  75. Turney, P.D.: Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews. In: Proceedings of Annual Meeting of the Association for Computational Linguistics, ACL 2002 (2002)Google Scholar
  76. Wang, B., Wang, H.: Bootstrapping both product features and opinion words from Chinese customer reviews with cross-inducing. In: Proceedings of the International Joint Conference on Natural Language Processing, IJCNLP 2008 (2008)Google Scholar
  77. Wang, H., Lu, Y., Zhai, C.: Latent aspect rating analysis on review text data: a rating regression approach. In: Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2010 (2010)Google Scholar
  78. Wei, W., Gulla, J.A.: Sentiment learning on product reviews via sentiment ontology tree. In: Proceedings of Annual Meeting of the Association for Computational Linguistics (ACL 2010) (2010)Google Scholar
  79. Wiebe, J., Riloff, E.: Creating subjective and objective sentence classifiers from unannotated texts. In: Proceedings of Computational Linguistics and Intelligent Text Processing, CICLing 2005 (2005)Google Scholar
  80. Wiebe, J., Wilson, T., Bruce, R., Bell, M., Martin, M.: Learning subjective language. Computational Linguistics 30(3), 277–308 (2004)CrossRefGoogle Scholar
  81. Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing contextual polarity in phrase-level sentiment analysis. In: Proceedings of the Human Language Technology Conference and the Conference on Empirical Methods in Natural Language Processing, HLT/EMNLP 2005 (2005)Google Scholar
  82. Wu, Y., Zhang, Q., Huang, X., Wu, L.: Phrase dependency parsing for opinion mining. In: Proceedings of Conference on Empirical Methods in Natural Language Processing, EMNLP 2009 (2009)Google Scholar
  83. Yi, J., Nasukawa, T., Bunescu, R., Niblack, W.: Sentiment analyzer: extracting sentiments about a given topic using natural language processing techniques. In: Proceedings of International Conference on Data Mining, ICDM 2003 (2003)Google Scholar
  84. Yu, H., Han, J., Chang, K.: PEBL: Positive example based learning for Web page classification using SVM. In: Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2002 (2002)Google Scholar
  85. Yu, J., Zha, Z., Wang, M., Chua, T.: Aspect ranking: identifying important product aspects from online consumer reviews. In: Proceedings of Annual Meeting of the Association for Computational Linguistics, ACL 2011 (2011a)Google Scholar
  86. Yu, J., Zha, Z., Wang, M., Wang, K., Chua, T.: Domain-Assisted product aspect hierarchy generation: towards hierarchical organization of unstructured consumer reviews. In: Proceedings of Conference on Empirical Methods in Natural Language Processing, EMNLP 2011 (2011b)Google Scholar
  87. Zhai, Z., Liu, B., Xu, H., Jia, P.: Clustering product features for opinion mining. In: Proceedings of ACM International Conference on Web Search and Data Mining, WSDM 2011 (2011)Google Scholar
  88. Zhai, Z., Liu, B., Xu, H., Jia, P.: Grouping product features using semi-supervised learning with soft-constraints. In: Proceedings of International Conference on Computational Linguistics, COLING 2010 (2010)Google Scholar
  89. Zhang, L., Liu, B., Lim, S., O’Brien-Strain, E.: Extracting and ranking product features in opinion documents. In: Proceedings of International Conference on Computational Linguistics, COLING 2010 (2010)Google Scholar
  90. Zhang, L., Liu, B.: Identifying noun product features that imply opinions. In: Proceedings of Annual Meeting of the Association for Computational Linguistics, ACL 2011 (2011a)Google Scholar
  91. Zhang, L., Liu, B.: Extracting resource terms for sentiment analysis. In: Proceedings of the International Joint Conference on Natural Language Processing, IJCNLP 2011 (2011b)Google Scholar
  92. Zhang, L., Liu, B.: Entity set expansion in opinion documents. In Proceedings of ACM Conference on Hypertext and Hypermedia (HT 2011) (2011c) Google Scholar
  93. Zhao, W., Jiang, J., Yan, H., Li, X.: Jointly modeling aspects and opinions with a MaxEnt-LDA hybrid. In: Proceedings of Conference on Empirical Methods in Natural Language Processing, EMNLP 2010 (2010)Google Scholar
  94. Zhu, J., Wang, H., Tsou, B.K., Zhu, M.: Multi-aspect opinion polling from textual reviews. In: Proceedings of ACM International Conference on Information and Knowledge Management, CIKM 2009 (2009)Google Scholar
  95. Zhuang, L., Jing, F., Zhu, X.: Movie review mining and summarization. In: Proceedings of ACM International Conference on Information and Knowledge Management, CIKM 2006 (2006)Google Scholar

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© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of Illinois at ChicagoChicagoUSA

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