Call for Paper - January 2024 Edition
IJCA solicits original research papers for the January 2024 Edition. Last date of manuscript submission is December 20, 2023. Read More

Detection and summarization of genuine review using Visual Data Mining

Print
PDF
 
Download Full Text
International Journal of Computer Applications
© 2012 by IJCA Journal
Volume 43 - Number 11
Year of Publication: 2012
Authors:
Jagruti Prajapati
Malay Bhatt
Dinesh J. Prajapati
10.5120/6148-8522

Jagruti Prajapati, Malay Bhatt and Dinesh J Prajapati. Article: Detection and summarization of genuine review using Visual Data Mining. International Journal of Computer Applications 43(11):22-26, April 2012. Full text available. BibTeX

@article{key:article,
	author = {Jagruti Prajapati and Malay Bhatt and Dinesh J. Prajapati},
	title = {Article: Detection and summarization of genuine review using Visual Data Mining},
	journal = {International Journal of Computer Applications},
	year = {2012},
	volume = {43},
	number = {11},
	pages = {22-26},
	month = {April},
	note = {Full text available}
}

Abstract

In earlier days we were asking our friends or relatives for their opinions regarding products which we want to purchase from the merchants. But now a day's E-commerce is gaining more and more popularity. Whatever query we are having, we can find its answer from World Wide Web. Merchants are also selling their products online and at a same time they are asking customer's review regarding products, which customer has bought. This would be beneficial to merchants as well as customers also. As the numbers of customers are growing, reviews received by products are also growing in large amount. Thus, mining opinions from product reviews is an important research topic. However, existing research is more focused towards classification and summarization of such online opinions. An important issue related to the trustworthiness of online opinions has been neglected most often. There is no reported study on assessing the trustworthiness of reviews. This research paper aims to first classify the opinion (positive or negative) carried out by detection of a review( spam or a non-spam ) based on rating behavior and finally removing spam reviews, which provides a trusted review to help the customer in taking appropriate buying decision. This paper proposes a novel and effective technique, which will represent classified opinion in form of "chernoff face".

References

  • Mukherjee, A. , Liu, B, Wang, J. , Glance, N, Jindal, N. Detecting Group Review Spam. Dept of CS. TechnicalReport, UIC, 2011
  • Bing Liu, Junhui Wang, Natalie Glance andNitin Jindal. Detecting Group Review Spam . WWW 2011, March 28–April 1, 2011, Hyderabad, India.
  • Lim, E. P. , Nguyen, V. A. , Jindal, N. , Liu, B. , and Lauw,H. Detecting product review spammers using rating behavior. CIKM, 2010.
  • N. Jindal, B. Liu, and E. -P. Lim. Finding unusual reviewpatterns using unexpected rules. CIKM,2010.
  • Siddu P. Algur, AmitP. Patil, P. S Hiremath and S. Shivashankar. Conceptual level Similarity Measure based Review Spam Detection. IEEE 2010.
  • Wei Jin, Hung Hay Ho and Rohini K. Srihari. OpinionMiner: A Novel Machine Learning System for Web Opinion Mining and Extraction. KDD'09, June 28–July 1, 2009, Paris, France. ACM 2009.
  • N. Jindal and B. Liu. Opinion spam and analysis. WSDM, 2008.
  • N. Jindal and B. Liu. Review spam detection. WWW (poster), 2007.
  • Minqing Hu and Bing Liu. Mining and Summarizing Customer Reviews. KDD'04, August 22–25, 2004, Seattle, Washington, USA. ACM 2004.
  • Peter D. Turney. Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews. Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (ACL), Philadelphia, July 2002, pp. 417-424.
  • Web data mining: Web Data Mining Exploring Hyperlinks, Contents, and Usage Data. By Bing liu, First Edition, Dec 2006, Springer.
  • http://www. amazon. com
  • Dataset available on: http://www. cs. uic. edu/~liub/FBS /sentiment-analysis. html
  • POS tagging Penntree bank link: http://www. ling. upenn. edu
  • NLP parser: http://nlp. stanford. edu
  • wordnet: http://wordnet. princeton. edu
  • MIT java api for English wordnet: http://projects. csail . mit. edu/jwi/
  • A Comparative Study of Visualization Techniques for Datamining:http://www. csse. monash. edu. au/~srini/theses/Redpath_Thesis. pdf
  • 2D, 3D and High-Dimensional Data and Information Visualization:http://www. iwi. unihannover. de/lv/seminar_ss05 /bartke/Assets/Paper. pdf