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Reseach Article

Cross Domain Sentiment Classification Techniques: A Review

by Parvati Kadli, Vidyavathi B. M.
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 181 - Number 37
Year of Publication: 2019
Authors: Parvati Kadli, Vidyavathi B. M.
10.5120/ijca2019918338

Parvati Kadli, Vidyavathi B. M. . Cross Domain Sentiment Classification Techniques: A Review. International Journal of Computer Applications. 181, 37 ( Jan 2019), 13-20. DOI=10.5120/ijca2019918338

@article{ 10.5120/ijca2019918338,
author = { Parvati Kadli, Vidyavathi B. M. },
title = { Cross Domain Sentiment Classification Techniques: A Review },
journal = { International Journal of Computer Applications },
issue_date = { Jan 2019 },
volume = { 181 },
number = { 37 },
month = { Jan },
year = { 2019 },
issn = { 0975-8887 },
pages = { 13-20 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume181/number37/30273-2019918338/ },
doi = { 10.5120/ijca2019918338 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:08:23.985805+05:30
%A Parvati Kadli
%A Vidyavathi B. M.
%T Cross Domain Sentiment Classification Techniques: A Review
%J International Journal of Computer Applications
%@ 0975-8887
%V 181
%N 37
%P 13-20
%D 2019
%I Foundation of Computer Science (FCS), NY, USA
Abstract

With the explosive growth in the availability of online resources, sentiment analysis has become an interesting topic for researchers working in the field of natural language processing and text mining. The social media corpus can span many different domains. It is difficult to get annotated data of all domains that can be used to train a learning model. Hence continuous efforts are made to tackle the issue and many techniques have been designed to improve cross domain sentiment analysis. In this paper we present literature review of methods and techniques employed for cross domain sentiment analysis. The aim of the review is to present an overview of techniques and approaches, datasets used to solve cross domain sentiment classification problem in the research work carried out in the recent years.

References
  1. Immonen, P. Pääkkönen, and E. Ovaska, ``Evaluating the Quality of Social Media Data in Big Data Architecture,'' IEEE Access, vol. 3,pp. 2028–2043,015.
  2. D. Jiang, X. Luo, J. Xuan, and Z. Xu, ``Sentiment computing for the news event based on the social media big data, “IEEE Access, vol. 5, pp. 2373–2382, 2016.
  3. M. N. Injadat, F. Salo, and A. B. Nassif, ``Data mining techniques in social media: A survey,'' Neurocomputing, vol. 214, pp. 654–670, Nov. 2016.
  4. T. A. A. Al-Moslmi, Machine Learning and Lexicon-Based Approach forArabic Sentiment Analysis. Bangi, Malaysia: Fakulti Teknologi & Sains Maklumat/Institut, 2014.
  5. N. Omar, M. Albared, T. Al-Moslmi, and A. Al-Shabi, ``A comparative study of feature selection and machine learning algorithms for Arabic sentiment classification,'' in Information Retrieval Technology. Springer, 2014, pp. 429–443.
  6. S. J. Pan, X. Ni, J.-T. Sun, Q. Yang, and Z. Chen, ``Cross-domain sentiment classification via spectral feature alignment,'' in Proc. 19th Int. Conf. Worldwide Web, vol. 10. 2010, p. 751.
  7. J. Blitzer, M. Dredze, and F. Pereira, ``Biographies, bollywood, boomboxes and blenders: Domain adaptation for sentiment classification,'' in Proc. ACL, vol. 7. 2007, pp. 440–447.
  8. B. Pang, L. Lee, and S. Vaithyanathan. Thumbs up? Sentiment classification using machine learning techniques. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, pages 79–86, 2002.
  9. S. Li and C. Zong, ``Multi-domain adaptation for sentiment classification: Using multiple classifier combining methods,'' in Proc. Int. Conf. Natural Lang. Process. Knowl. Eng. (NLP-KE), 2008, pp. 1–8.
  10. F Bisio, P. Gastaldo, C. Peretti, R. Zunino, and E. Cambria, ``Data intensive review mining for sentiment classification across heterogeneous domains,'' in Proc. IEEE/ACM Int. Conf. Adv. Social Netw. Anal. Mining, vol. 13.Aug. 2013, pp. 1061–1067.
  11. J. Blitzer, R. McDonald, and F. Pereira, ``Domain adaptation with structural correspondence learning,'' in Proc. Conf. Empirical Methods Natural Lang. Process., 2006, pp. 120–128.
  12. M. Franco-Salvador, F. L. Cruz, J. A. Troyano, and P. Rosso, ``Cross-domain polarity classification using a knowledge-enhanced meta classifier,'' Knowl.-Based Syst., vol. 86, pp. 46–56, Jun. 2015.
  13. Y. He, C. Lin, and He, H. Alani, ``Automatically extracting polarity-bearing topics for cross-domain sentiment classification,” in Proc.49th Annual meeting, Jun 2011,pp. 123–131.
  14. D. M. Blei and M. I. Jordan, ``Modeling annotated data,'' in Proc. 26th Annu. Int. ACM SIGIR Conf. Res. Develop. Inf. Retrieval, 2003, pp. 127–134.
  15. Y. He, C. Lin, W. Gao, and K.-F. Wong, ``Dynamic joint sentiment-topic model,'' ACM Trans. Intell. Syst. Technol., vol. 5, no. 1, p. 6, 2013.
  16. B. Settles, Active Learning Literature Survey. Madison, WI, USA: Univ. Wisconsin, 2010.
  17. S. Li, Y. Xue, Z. Wang, and G. Zhou, ``Active learning for cross-domain sentiment classification,'' in Proc. 23rd Int. Joint Conf. Artif. Intell., 2013,pp. 2127–2133.
  18. X. Glorot, A. Bordes, and Y. Bengio, ``Domain adaptation for large-scale sentiment classification: A deep learning approach,'' in Proc. 28th Int. Conf. Mach. Learn., 2011, pp. 513–520.
  19. J.Carrillo de Albornoz, L. Plaza, and P. Gervás, ``A hybrid approach to emotional sentence polarity and intensity classification,'' in Proc. 14th Conf. Comput. Natural Lang. Learn., 2010, pp. 153–161.
  20. J. Blitzer, M. Dredze, F. Pereira, Biographies, bollywood, boomboxes and blenders: Domain adaptation for sentiment classification, in: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics, 2007, pp. 187–205.
  21. G.-R. Xue, W. Dai, Q. Yang, and Y. Yu, ``Topic-bridged PLSA for crossdomain text classification,'' in Proc. 31st Annu. Int. ACM SIGIR Conf. Res. Develop. Inf. Retrieval, 2008, pp. 627–634.
  22. F. Zhuang et al., ``Collaborative dual-PLSA: Mining distinction and commonality across multiple domains for text classification,” in Proc. 19th ACM Int. Conf. Inf. Knowl. Manage., 2010, pp. 359–368.
  23. Q. Wu and S. Tan, ``A two-stage framework for cross-domain sentiment classification,'' Expert Syst. Appl., vol. 38, no. 11, pp. 14269–14275, 2011.
  24. G. Zhou, Y. Zhou, X. Guo, X. Tu, and T. He, ``Cross-domain sentiment classification via topical correspondence transfer,” Neurocomputing, vol. 159, pp. 298–305, Dec. 2015.
  25. P. Yang, W. Gao, Q. Tan, and K.-F. Wong, ``A link- bridged topic model for cross-domain document classification,'' Inf. Process. Manage., vol. 49, no. 6, pp. 1181–1193.Nov.2013.
  26. S. D. Roy, T. Mei, W. Zeng, and S. Li, ``Social Transfer: Cross-domain transfer learning from social streams for media applications,'' in Proc. 20th ACM Int. Conf. Multimedia, 2012, pp. 649–658.
  27. R. Zhao and K. Mao, ``Supervised adaptive-transfer PLSA for cross domain text classification,'' in Proc. IEEE Int. Conf. Data Mining Work shop, Jan. 2014, pp. 259–266.
  28. J. Liang, K. Zhang, X. Zhou, Y. Hu, J. Tan, and S. Bai, ``Leveraging latent sentiment constraint in probabilistic matrix factorization for cross domain sentiment classification,'' Proc. Comput. Sci., vol. 80, pp. 366–375, Mar. 2016.
  29. D. Bollegala, D. Weir, and J. Carroll, ``Cross-domain sentiment classification using a sentiment sensitive thesaurus,'' IEEE Trans. Knowl. DataEng., vol. 5, no. 8, pp. 1719–1731, Aug. 2013.
  30. D. Bollegala, T. Mu, and J. Y. Goulermas, ``Cross-domain sentiment classification using sentiment sensitive embeddings,'' IEEE Trans. Knowl. Data Eng., vol. 28, no. 2, pp. 398–410, Feb. 2016.
  31. N. X. Bach, V. T. Hai, and T. M. Phuong, ``Cross-domain sentiment classification with word embeddings and canonical correlation analysis,'' in Proc. 7th Symp. Inf. Commun. Technol., 2016, pp. 159–166.
  32. B. Ohana, S. J. Delany, and B. Tierney, ``A case-based approach to cross domain sentiment classification,'' in Lecture Notes in Computer Science, vol. 7466. Cham, Switzerland: Springer, 2012, pp. 284–296.
  33. H. Hammer, A.Yazidi, A. Bai, and P. Engelstad, ``Building domain specific sentiment lexicons combining information from many sentiment lexicons and a domain specific corpus,'' Comput. Sci. Appl., vol. 456, pp. 205–216, Dec. 2015.
  34. A Tsakalidis, ``An ensemble model for cross-domain polarity classification on twitter,'' in Web Information Systems Engineering. Cham, Switzerland: Springer, 2014, pp. 168–177.
  35. Y. Zhang, X. Hu, P. Li, L. Li, and X. Wu, ``Cross-domain sentiment classification-feature divergence, polarity divergence or both?'' Pattern Recognit. Lett., vol. 65, pp. 44–50, Jun. 2015.
  36. X. Zhu and Z. Ghahramani, Learning From Labeled and Unlabeled Data With Label Propagation. Seattle, WA, USA: Semantic Scholar, 2002.
  37. N. Ponomareva and M. Thelwall, ``Do neighbours help?: An exploration of graph-based algorithms for cross- domain sentiment classification,'' in Proc. Joint Conf. Empirical Methods Natural Lang. Process. Comput Natural Lang. Learn., Jul. 2012, pp. 655–665.
  38. Z. Zhu, D. Dai, Y. Ding, J. Qian, and S. Li, ``Employing emotion keywords to improve cross- domain sentiment classification,'' in Proc. Chin. Lexical Semantics, 2013, pp. 64–71.
  39. R. Remus, ``Domain adaptation using domain similarity- and domain complexity-based instance selection for cross-domain sentiment analysis,'' in Proc.-12th IEEE Int. Conf. Data Mining Workshops, Jun. 2012, pp. 717–723.
  40. N. Ponomareva and M. Thelwall, ``Biographies or blenders: Which resource is best for cross-domain sentiment analysis?'' in Lecture Notes in Computer Science, vol. 7181. Cham, Switzerland: Springer, 2012, pp. 488–499.
  41. R. Xia, C. Zong, X. Hu, and E. Cambria, ``Feature ensemble plus sample selection: Domain adaptation for sentiment classi_cation,'' IEEE Intell. Syst., vol. 28, no. 3, pp. 10–18, May 2013.
  42. N. Ponomareva and M. Thelwall, ``Semi-supervised vs. cross-domain graphs for sentiment analysis,'' in Proc. RANLP, 2013, pp. 571–578.
  43. Y. Tsai, R. T. Tsai, C. Chueh, and S. Chang, ``Cross-domain opinion word identification with query-by-committee active learning,'' in Technologies and Applications of Artificial Intelligence. Cham, Switzerland: Springer, 2014, pp. 334–343.
  44. C. Lin, Y. Lee, C. Yu, and H. Chen, ``Exploring ensemble of models in taxonomy-based cross-domain sentiment classification,'' in Proc. 23rd ACM Int. Conf. Conf. Inf. Knowl. Manage.-(CIKM), 2014, pp. 1279–1288.
Index Terms

Computer Science
Information Sciences

Keywords

Cross Domain Sentiment Classification (CDSC) Source Domain Target Domain.