Skip to main content
SpringerLink
Log in

A Comprehensive Study on Opinion Mining Features and Their Applications

  • Conference paper
  • First Online:
Book cover Recent Trends in Information and Communication Technology (IRICT 2017)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 5))

Abstract

E-shopping is a modern approach to buy or sell products/services and becomes so popular with growing the Internet. So, opinion mining becomes a very important concept in data mining area as researchers and business usually need to know about the overall sentiment of viewpoint of people about desired phenomena. Opinion mining is the fundamental phase for variety data mining applications such as opinion summarization, recommendation system and opinion spam detection. To achieve the best results of opinion mining, we need to use the proper set of features for classification and clustering. In this paper, we do comprehensive investigation on various types of features exploited in variety sub-branches of opinion mining domain. We present the most frequent features sets includes structural, linguistic and relation-based features as a comprehensive reference for further opinion mining research. The results proved that using multiple types of features improve the accuracy of opinion mining applications.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Ho-Dac, N.N., Carson, S.J., Moore, W.L.: The effects of positive and negative online customer reviews: do brand strength and category maturity matter? J. Mark. 77(6), 37–53 (2013)

    Article  Google Scholar 

  2. Zhu, F., Zhang, X.: Impact of online consumer reviews on sales: the moderating role of product and consumer characteristics. J. Mark. 74(2), 133–148 (2010)

    Article  Google Scholar 

  3. Pennebaker, J.W., King, L.A.: Linguistic styles: language use as an individual difference. J. Pers. Soc. Psychol. 6, 1296–1312 (1999)

    Article  Google Scholar 

  4. Shapiro, D.: Psychotherapy of Neurotic Character. Basic Books, New York (1989)

    Google Scholar 

  5. Evelyn, J.: Online Shopping-Unabridged Guide. Emereo Publishing (2012)

    Google Scholar 

  6. Algur, S.P., Patil, A.P., Hiremath, P.S., Shivashankar, S.: Conceptual level similarity measure based review spam detection. In: 2010 International Conference on Signal and Image Processing (ICSIP), pp. 416–423. IEEE, December 2010

    Google Scholar 

  7. McCallum, A.K.: Bow: a toolkit for statistical language modeling, text retrieval, classification and clustering (1996). http://www.cs.cmu.edu/~mccallum/bow

  8. Porter, M.F.: An algorithm for suffix stripping. Program 14, 130–137 (1980)

    Article  Google Scholar 

  9. Dave, K., Lawrence, S., Pennock, D.M.: Mining the peanut gallery: opinion extraction and semantic classification of product reviews. In: Proceedings of the 12th International Conference on World Wide Web, pp. 519–528. ACM, May 2003

    Google Scholar 

  10. Jindal, N., Liu, B.: Analyzing and detecting review spam. In: Seventh IEEE International Conference on Data Mining, ICDM 2007, pp. 547–552. IEEE, October 2007

    Google Scholar 

  11. Wang, G., Xie, S., Liu, B. Yu, P.S.: Review graph based online store review spammer detection. In: 2011 IEEE 11th International Conference on Data Mining (ICDM), pp. 1242–1247. IEEE, December 2011

    Google Scholar 

  12. Ghose, A., Ipeirotis, P.G., Sundararajan, A.: Opinion mining using econometrics: a case study on reputation systems. In: Annual Meeting-Association for Computational Linguistics, vol. 45, no. 1, p. 416, June 2007

    Google Scholar 

  13. Akoglu, L., Chandy, R., Faloutsos, C.: Opinion fraud detection in online reviews by network effects. ICWSM 13, 2–11 (2013)

    Google Scholar 

  14. Hammad, A.A., El-Halees, A.: An approach for detecting spam in arabic opinion reviews. Int. Arab J. Inf. Technol. 12(1), 10–16 (2015)

    Google Scholar 

  15. D’onfro, J.: A Whopping 20% of Yelp Reviews are Fake (2013). http://read.bi/1M03jxl

  16. Chen, Y.R., Chen, H.H.: Opinion spam detection in web forum: a real case study. In: Proceedings of the 24th International Conference on World Wide Web, pp. 173–183. ACM, May 2015

    Google Scholar 

  17. Jindal, N., Liu, B.: Review spam detection. In: Proceedings of the 16th International Conference on World Wide Web, pp. 1189–1190. ACM, May 2007

    Google Scholar 

  18. Li, J., Ott, M., Cardie, C., Hovy, E.H.: Towards a general rule for identifying deceptive opinion spam. In: ACL, vol. 1, pp. 1566–1576, June 2014

    Google Scholar 

  19. Chen, Y.R., Chen, H.H.: Opinion spammer detection in web forum. In: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 759–762. ACM, August 2015

    Google Scholar 

  20. Li, F., Huang, M., Yang, Y., Zhu, X.: Learning to identify review spam. In: IJCAI Proceedings-International Joint Conference on Artificial Intelligence, vol. 22, no. 3, p. 2488, July 2011

    Google Scholar 

  21. Yoo, K.H., Gretzel, U.: Comparison of deceptive and truthful travel reviews. Inf. Commun. Technol. Tourism 2009, 37–47 (2009)

    Google Scholar 

  22. Newman, M.L., Pennebaker, J.W., Berry, D.S., Richards, J.M.: Lying words: predicting deception from linguistic styles. Pers. Soc. Psychol. Bull. 29(5), 665–675 (2003)

    Article  Google Scholar 

  23. Lu, Y., Zhang, L., Xiao, Y., Li, Y.: Simultaneously detecting fake reviews and review spammers using factor graph model. In: Proceedings of the 5th Annual ACM Web Science Conference, pp. 225–233. ACM, May 2013

    Google Scholar 

  24. Thanikkal, J.G., Danish, M.: A novel approach to improve spam detection using SDS algorithm. Int. J. Innov. Res. Sci. Technol. 1(12), 306–310 (2015)

    Google Scholar 

  25. Mukherjee, A., Venkataraman, V., Liu, B., Glance, N.S.: What yelp fake review filter might be doing? In: ICWSM (2013)

    Google Scholar 

  26. Kim, S.-M., Pantel, P., Chklovski, T., Pennacchiotti, M.: Automatically assessing review helpfulness. In: EMNLP (2006)

    Google Scholar 

  27. Lim, E.P., Nguyen, V.A., Jindal, N., Liu, B., Lauw, H.W.: Detecting product review spammers using rating behaviors. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, pp. 939–948. ACM, October 2010

    Google Scholar 

  28. Popescu, A.-M., Etzioni, O.: Extracting product features and opinions from reviews. In: EMNLP 2005 (2005)

    Google Scholar 

  29. Ott, M., Choi, Y., Cardie, C., Hancock, J.T.: Finding deceptive opinion spam by any stretch of the imagination. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, vol. 1, pp. 309–319. Association for Computational Linguistics, June 2011

    Google Scholar 

  30. Sun, H., Morales, A., Yan, X.: Synthetic review spamming and defense. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and data mining, pp. 1088–1096. ACM, August 2013

    Google Scholar 

  31. Mukherjee, A., Liu, B., Glance, N.: Spotting fake reviewer groups in consumer reviews. In: Proceedings of the 21st International Conference on World Wide Web, pp. 191–200. ACM, April 2012

    Google Scholar 

  32. Zhang, Z., Varadarajan, B.: Utility scoring of product reviews. In: Proceedings of the 15th ACM International Conference on Information and Knowledge Management, pp. 51–57. ACM, November 2006

    Google Scholar 

  33. Li, H., Chen, Z., Liu, B., Wei, X., Shao, J.: Spotting fake reviews via collective PU learning. In: ICDM (2014)

    Google Scholar 

  34. Mukherjee, A., Venkataraman, V.: Opinion Spam Detection: An Unsupervised Approach Using Generative Models. Techincal Report, UH (2014)

    Google Scholar 

  35. Wang, T., Zhu, H.: Voting for deceptive opinion spam detection. arXiv preprint arXiv:1409.4504 (2014)

  36. Fei, G., Mukherjee, A., Liu, B., Hsu, M., Castellanos, M., Ghosh, R.: Exploiting burstiness in reviews for review spammer detection. In: ICWSM, June 2013

    Google Scholar 

  37. Mukherjee, A., Kumar, A., Liu, B., Wang, J., Hsu, M., Castellanos, M., Ghosh, R.: Spotting opinion spammers using behavioral footprints. In: KDD, pp. 632–640. ACM (2013)

    Google Scholar 

  38. Xu, Y., Shi, B., Tian, W., Lam, W.: A unified model for unsupervised opinion spamming detection incorporating text generality (2015)

    Google Scholar 

  39. Lin, Y., Zhu, T., Wang, X., Zhang, J., Zhou, A.: Towards online review spam detection. In: Proceedings of the 23rd International Conference on World Wide Web, pp. 341–342. ACM, April 2014

    Google Scholar 

  40. Lin, Y., Zhu, T., Wu, H., Zhang, J., Wang, X. and Zhou, A., 2014, August. Towards online anti-opinion spam: spotting fake reviews from the review sequence. In: 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 261–264. IEEE, August 2014

    Google Scholar 

  41. Ott, M., Cardie, C., Hancock, J.: Estimating the prevalence of deception in online review communities. In: Proceedings of the 21st International Conference on World Wide Web, pp. 201–210. ACM, April 2012

    Google Scholar 

  42. Jindal, N., Liu, B.: Opinion spam and analysis. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 219–230. ACM, February 2008

    Google Scholar 

  43. Hu, M., Liu, B.: Mining and summarizing customer reviews. In: KDD 2004

    Google Scholar 

  44. Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231–240. ACM, February 2008

    Google Scholar 

  45. Patel, R., Thakkar, P.: Opinion spam detection using feature selection. In: 2014 International Conference on Computational Intelligence and Communication Networks (CICN), pp. 560–564. IEEE, November 2014

    Google Scholar 

  46. Xie, S., Wang, G., Lin, S., Yu, P.S.: Review spam detection via temporal pattern discovery. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 823–831. ACM, August 2012

    Google Scholar 

  47. Dellarocas, C.: Immunizing online reputation reporting systems against unfair ratings and discriminatory behavior. In: ACM EC (2000)

    Google Scholar 

  48. Wu, G., Greene, D., Smyth, B., Cunningham, P.: Distortion as a validation criterion in the identification of suspicious reviews. Technical report UCD-CSI-2010-04, University College Dublin (2010)

    Google Scholar 

  49. Mukherjee, A., Liu, B., Wang, J., Glance, N., Jindal, N.: Detecting group review spam. In: Proceedings of the 20th International Conference Companion on World Wide Web, pp. 93–94. ACM, March 2011

    Google Scholar 

  50. Sahami, M., Dumais, S., Heckerman, D., Horvitz, E.: A Bayesian approach to filtering junk {e}-mail. AAAI Technical report WS-98-05 (1998)

    Google Scholar 

  51. Li, H., Chen, Z., Mukherjee, A., Liu, B., Shao, J.: Analyzing and detecting opinion spam on a large-scale dataset via temporal and spatial patterns. In: Proceedings of the 9th International AAAI Conference on Web and Social Media, ICWSM 2015, Oxford, UK, pp. 26–29, May 2015

    Google Scholar 

Download references

Acknowledgments

This work is supported by Ministry of Higher Education (MOHE) and Research Management Centre (RMC) at the Universiti Teknologi Malaysia (UTM) under Research University Grant Category (R.J130000.7828.4F719).

Author information

Authors and Affiliations

  1. Faculty of Computing, Universiti Teknologi of Malaysia, UTM, 81300, Johor, Malaysia

    Shirin Noekhah, Naomie Binti Salim & Nor Hawaniah Zakaria

Authors
  1. Shirin Noekhah
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Naomie Binti Salim
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nor Hawaniah Zakaria
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shirin Noekhah .

Editor information

Editors and Affiliations

  1. Faculty of Computing Information Systems Department, Universiti Teknologi Malaysia, Johar, Skudai, Malaysia

    Faisal Saeed

  2. Faculty of Computing Information Systems Department, Universiti Teknologi Malaysia, Johar, Skudai, Malaysia

    Nadhmi Gazem

  3. Department of Computer Science and Engineering, SOA University, Bhubaneswar, Odisha, India

    Srikanta Patnaik

  4. Faculty of Computing, ISSI Research Lab, University of Technology Malaysia, Skudai, Malaysia

    Ali Saleh Saed Balaid

  5. Faculty of Computing, University of Technology Malaysia, Skudai, Malaysia

    Fathey Mohammed

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Cite this paper

Noekhah, S., Salim, N.B., Zakaria, N.H. (2018). A Comprehensive Study on Opinion Mining Features and Their Applications. In: Saeed, F., Gazem, N., Patnaik, S., Saed Balaid, A., Mohammed, F. (eds) Recent Trends in Information and Communication Technology. IRICT 2017. Lecture Notes on Data Engineering and Communications Technologies, vol 5. Springer, Cham. https://doi.org/10.1007/978-3-319-59427-9_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-59427-9_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59426-2

  • Online ISBN: 978-3-319-59427-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation