Journal of Internet Computing and Services (인터넷정보학회논문지)

Korean Society for Internet Information (한국인터넷정보학회)
권호 표지
DOI QR코드

DOI QR Code

A Review of the Opinion Target Extraction using Sequence Labeling Algorithms based on Features Combinations

  • Received : 2016.05.09
  • Accepted : 2016.06.17
  • Published : 2016.10.31
Download PDF
⟨ Previous Next ⟩

Abstract

In recent years, the opinion analysis is one of the key research fronts of any domain. Opinion target extraction is an essential process of opinion analysis. Target is usually referred to noun or noun phrase in an entity which is deliberated by the opinion holder. Extraction of opinion target facilitates the opinion analysis more precisely and in addition helps to identify the opinion polarity i.e. users can perceive opinion in detail of a target including all its features. One of the most commonly employed algorithms is a sequence labeling algorithm also called Conditional Random Fields. In present article, recent opinion target extraction approaches are reviewed based on sequence labeling algorithm and it features combinations by analyzing and comparing these approaches. The good selection of features combinations will in some way give a good or better accuracy result. Features combinations are an essential process that can be used to identify and remove unneeded, irrelevant and redundant attributes from data that do not contribute to the accuracy of a predictive model or may in fact decrease the accuracy of the model. Hence, in general this review eventually leads to the contribution for the opinion analysis approach and assist researcher for the opinion target extraction in particular.

Keywords

References

  1. Samovar, L., et al., "Communication between cultures". 2015: Nelson Education.
  2. Gil de Zuniga, H., L. Molyneux, and P. Zheng, "Social media, political expression, and political participation: Panel analysis of lagged and concurrent relationships". Journal of Communication, 64(4):pp. 612-634, 2014. http://dx.doi.org/10.1111/jcom.12103
  3. Liu, B., "Sentiment Analysis and Subjectivity". Handbook of natural language processing, 2010. 2:pp. 627-666.
  4. Jimenez-Zafra, S.M., et al., "Combining resources to improve unsupervised sentiment analysis at aspect-level". Journal of Information Science, 2015. http://dx.doi.org/10.1177/0165551515593686
  5. Dave, S. and H. Diwanji, "Trend Analysis in Social Networking using Opinion Mining A Survey". 2015.
  6. Pang, B. and L. Lee, "Opinion Mining and Sentiment Analysis". Found. Trends Inf. Retr., 2(1-2), pp. 1-135, 2008. http://dx.doi.org/10.1561/1500000011
  7. Osimo, D. and F. Mureddu, "Research challenge on opinion mining and sentiment analysis". Universite de Paris-Sud, Laboratoire LIMSI-CNRS, Batiment, 508, 2012.
  8. Liu, B., "Web Data Mining", Springer, 2006.
  9. Kim, S.-M. and E. Hovy. "Determining the sentiment of opinions". in Proceedings of the 20th international conference on Computational Linguistics, Association for Computational Linguistics, 2004. http://dx.doi.org/10.3115/1220355.1220555
  10. Yu, H. and V. Hatzivassiloglou. "Towards answering opinion questions: Separating facts from opinions and identifying the polarity of opinion sentences". in Proceedings of the 2003 conference on Empirical methods in natural language processing, Association for Computational Linguistics, 2003. http://dx.doi.org/10.3115/1119355.1119372
  11. Gezici, G., et al., "Sentiment Analysis Using Domain-Adaptation and Sentence-Based Analysis", in Advances in Social Media Analysis, Springer, pp. 45-64, 2015. http://dx.doi.org/10.1007/978-3-319-18458-6_3
  12. Pang, B., L. Lee, and S. Vaithyanathan. "Thumbs up?: sentiment classification using machine learning techniques". in Proceedings of the ACL-02 conference on Empirical methods in natural language processing-Volume 10, Association for Computational Linguistics, 2002. http://dx.doi.org/10.3115/1118693.1118704
  13. Tang, D., "Sentiment-Specific Representation Learning for Document-Level Sentiment Analysis", in Proceedings of the Eighth ACM International Conference on Web Search and Data Mining, ACM: Shanghai, China. pp. 447-452, 2015. http://dx.doi.org/10.1145/2684822.2697035
  14. Hu, M. and B. Liu. "Mining and summarizing customer reviews". in Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining. 2004: ACM. http://dx.doi.org/10.1145/1014052.1014073
  15. Sheng, H., et al. "Fine-grained Product Features Extraction and Categorization in Reviews Opinion Mining". in Data Mining Workshops (ICDMW), 2012 IEEE 12th International Conference on. 2012. http://dx.doi.org/10.1109/ICDMW.2012.53
  16. Patra, B.G., et al., "Ju_cse: A conditional random field (crf) based approach to aspect based sentiment analysis". SemEval 2014, pp. 370, 2014.
  17. Samha, A.K., Y. Li, and J. Zhang, "Aspect-based opinion mining from product reviews using conditional random fields", 2015.
  18. Rohrdantz, C., et al., "Feature-based visual sentiment analysis of text document streams". ACM Transactions on Intelligent Systems and Technology (TIST), 3(2), pp. 26, 2012. http://dx.doi.org/10.1145/2089094.2089102
  19. Saha, S. and A. Ekbal, "Combining Multiple Classifier using Voted based Classifier Ensemble Technique for Named Entity Recognition". Data & Knowledge Engineering, 85, pp. 15-39, 2013. http://dx.doi.org/10.1016/j.datak.2012.06.003
  20. Rocktaschel, T., M. Weidlich, and U. Leser, "ChemSpot: a hybrid system for chemical named entity recognition", Bioinformatics, 28(12), pp. 1633-1640, 2012. http://dx.doi.org/10.1093/bioinformatics/bts183
  21. Huang, F., et al., "Learning representations for weakly supervised natural language processing tasks", Computational Linguistics, 40(1), pp. 85-120, 2014. http://dx.doi.org/10.1162/COLI_a_00167
  22. Tachioka, Y., et al., "Discriminative methods for noise robust speech recognition: A CHiME challenge benchmark", Proc. CHiME, pp. 19-24, 2013.
  23. Farabet, C., et al., "Learning hierarchical features for scene labeling", Pattern Analysis and Machine Intelligence, IEEE Transactions on, 35(8), pp. 1915-1929, 2013. http://dx.doi.org/10.1109/TPAMI.2012.231
  24. Lu, W., et al., "CRF-TM: A Conditional Random Field Method for Predicting Transmembrane Topology", in Intelligence Science and Big Data Engineering. Big Data and Machine Learning Techniques, Springer, pp. 529-537, 2015. http://dx.doi.org/10.1007/978-3-319-23862-3_52
  25. Lukov, L., et al., "Protein Secondary Structure Prediction with Conditional Random Fields", School of Information Technologies, University of Sydney, 2010.
  26. Wang, L. and U.H. Sauer, "OnD-CRF: predicting order and disorder in proteins conditional random fields", Bioinformatics, 24(11), pp. 1401-1402, 2008. https://doi.org/10.1093/bioinformatics/btn132
  27. Hamdan, H., P. Bellot, and F. Bechet, "Lsislif: CRF and Logistic Regression for Opinion Target Extraction and Sentiment Polarity Analysis", 2015. http://dx.doi.org/10.1093/bioinformatics/btn132
  28. Wang, C., et al. "Opinion Mining Research on Chinese Micro-blog". in First International Conference on Information Science and Electronic Technology (ISET 2015), Atlantis Press, 2015.
  29. Yongmei, S. and H. Hua, "Research on Domain-independent Opinion Target Extraction", International Journal of Hybrid Information Technology, 8(1), pp. 237-246, 2015. https://doi.org/10.14257/ijhit.2015.8.1.21
  30. Zhou, X., X. Wan, and J. Xiao. "Cross-Language Opinion Target Extraction in Review Texts", in Data Mining (ICDM), 2012 IEEE 12th International Conference on. 2012. http://dx.doi.org/10.1109/ICDM.2012.32
  31. Jakob, N. and I. Gurevych. "Extracting opinion targets in a single-and cross-domain setting with conditional random fields". in Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, 2010.
  32. Lafferty, J., A. McCallum, and F.C. Pereira, "Conditional random fields: Probabilistic models for segmenting and labeling sequence data", 2001.
  33. Wallach, H.M., "Conditional random fields: An introduction", Technical Reports (CIS), pp. 22, 2004.
  34. Carstairs-McCarthy, A., "An introduction to English morphology: words and their structure", Edinburgh University Press, 2002.
  35. Dingare, S., et al., "A system for identifying named entities in biomedical text: How results from two evaluations reflect on both the system and the evaluations". Comparative and Functional Genomics, 6(1-2), pp. 77-85, 2005. https://doi.org/10.1002/cfg.457
  36. Manning, C.D., et al., "Introduction to Information Retrieval", Cambridge University Press, 496, 2008.
  37. Perlmutter, D.M., "Deep and Surface Structure Constraints in Syntax", Language, 49(3), pp. 697-701, 1973. https://doi.org/10.2307/412360
  38. Yi, J., et al. "Sentiment analyzer: Extracting sentiments about a given topic using natural language processing techniques", in Data Mining, ICDM 2003. Third IEEE International Conference on. 2003 IEEE. http://dx.doi.org/10.1109/ICDM.2003.1250949
  39. Asch, V.V. and W. Daelemans, "Prepositional Phrase Attachment in Shallow Parsing", International Conference RANLP 2009, pp. 12-17, 2009.
  40. Shi, H., "Research on Fine-grained Sentiment Analysis", Soochow University, pp. 46-49, 2013.
  41. Del Corro, L. and R. Gemulla. "ClausIE: clause-based open information extraction", in Proceedings of the 22nd international conference on World Wide Web, International World Wide Web Conferences Steering Committee, 2013. http://dx.doi.org/10.1145/2488388.2488420
  42. Kessler, J.S. and N. Nicolov. "Targeting Sentiment Expressions through Supervised Ranking of Linguistic Configurations", in ICWSM. 2009.
  43. Bechet, F., "Named Entity Recognition", in Spoken Language Understanding, John Wiley & Sons, Ltd. pp. 257-290, 2011. http://dx.doi.org/10.1002/9781119992691.ch10
  44. Singh, S., et al., "Analysis of Anaphora Resolution System for English Language", International Journal on Information Theory (IJIT), 3(2), 2014.