Call for Paper - March 2023 Edition
IJCA solicits original research papers for the March 2023 Edition. Last date of manuscript submission is February 20, 2023. Read More

A Review on Different Opinion and Aspect Mining Techniques

Print
PDF
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Authors:
Devi Venugopal, Remya R.
10.5120/ijca2016908127

Devi Venugopal and Remya R.. Article: A Review on Different Opinion and Aspect Mining Techniques. International Journal of Computer Applications 133(15):1-4, January 2016. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

@article{key:article,
	author = {Devi Venugopal and Remya R.},
	title = {Article: A Review on Different Opinion and Aspect Mining Techniques},
	journal = {International Journal of Computer Applications},
	year = {2016},
	volume = {133},
	number = {15},
	pages = {1-4},
	month = {January},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}
}

Abstract

With the rising popularity of internet, online drug reviews have been proved to be extremely helpful for patients suffering from chronic diseases. Most of the patients search upon online reviews before taking any medicine. Online reviews, blogs, and discussion forums such as WebMD on chronic diseases and medicines are becoming important supporting resources for patients. Extracting useful information from these reviews is very difficult and challenging. Opinion mining or aspect mining involves the extraction of useful information (e.g. positive or negative sentiments of a product) from a large quantity of text opinions or reviews given by Internet users. Various algorithms had been proposed to extract information from the opinion of web users. Some of the algorithms are LDA, sLDA, NMF, SSNMF, DiscLDA and PAAM. A detailed review of the most important opinion mining algorithms is presented and a comparison among the discussed techniques is given.

References

  1. B. Pang and L. Lee, ”Opinion mining and sentiment analysis”, Trends Inf. Ret., vol. 2, no. 12, pp. 1135, Jan. 2008.
  2. D.Mimno and A.McCallum, ”Topic models conditioned on arbitary features with Dirichlet multinomial regression”, in Proc.24th Conf. Uncertain. Artif. Intell., 2008.
  3. D. Blei, A. Ng, and M. Jordan, ”Latent Dirichlet allocation”, J. Mach. Learn. Res., vol. 3, pp. 9931022, Jan. 2003.X. P. Zhang, Separable reversible data hiding in encrypted image, IEEE Trans. Inf. Forensics Security, vol. 7, no. 2, pp. 826832, Apr.2012.
  4. C. Lin and Y. He, ”Joint sentiment/topic model for sentiment analysis”, in Proc. 18th ACM CIKM, New York, NY, USA, 2009, pp. 375384.
  5. Y. Jo and A. Oh, ”Aspect and sentiment unification model for online review analysis”, in Proc. 4th ACM Int. Conf. WSDM, New York, NY, USA, 2011, pp. 815824.
  6. D. Blei and J. Mcauliffe, ”Supervised topic models”, in Proc. Adv. NIPS, 2007, pp. 121128.
  7. S. Lacoste-Julien, F. Sha, andM. Jordan, ”DiscLDA: Discriminative learning for dimensionality reduction and classification”, in Proc. Adv. NIPS, 2008, pp. 897904.
  8. D. Ramage, D. Hall, R. Nallapati, and C. Manning, ”Labeled LDA: A supervised topic model for credit attribution in multilabeled corpora”, in Proc. Conf. EMNLP, Stroudsburg, PA, USA, 2009, pp. 248256.
  9. W. Xu, X. Liu, and Y. Gong, ”Document clustering based on non-negative matrix factorization”, in Proc. 26th Annu. Int. ACM SIGIR Conf. Res. Develop. Inform. Ret., New York, NY, USA, 2003, pp. 267273.
  10. H. Lee, J. Yoo, and S. Choi, ”Semi-supervised nonnegative matrix factorization”, IEEE Signal Process. Lett., vol. 17, no. 1, pp. 47,Jan. 2010.
  11. Victor C. Cheng, C.H.C. Leung, Jiming Liu, Fellow, IEEE, and Alfredo Milani, ”Probabilistic Aspect based mining model for drug reviews”, in: Proceedings of IEEE transactions on Knowledge and Data Engineering, Vol. 26, No. 8, August 2014.
  12. K. Denecke and W. Nejdl, ”How valuable is medical social media data? content analysis of the medical web”, J. Inform. Sci., vol. 179, no. 12, pp. 18701880, 2009.
  13. X. Ma, G. Chen, and J. Xiao, ”Analysis on an online health social network”, in Proc. 1st ACM Int. Health Inform. Symp., New York, NY, USA, 2010, pp. 297306.
  14. A. Nvol and Z. Lu, ”Automatic integration of drug indications from multiple health resources”, in Proc. 1st ACM Int. Health Inform. Symp., New York, NY, USA, 2010, pp. 666673.
  15. J. Leimeister, K. Schweizer, S. Leimeister, and H. Krcmar, ”Do virtual communities matter for the social support of patients? Antecedents and effects of virtual relationships in online communities”, Inform. Technol. People, vol. 21, no. 4, pp. 350374, 2008.
  16. C. Manning and H. Schtze, ”Foundations of Statistical Natural Language Processing” Cambridge, MA, USA: MIT Press, 1999.

Keywords

Aspect Mining, Drug Reviews, Opinion Mining, Text Mining, Topic Modeling