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Reseach Article

User Personalization based Product Ranking using Sentimental Reviews

Published on July 2018 by Namrata R. Bhamre, Nitin N. Patil
International Conference on “Internet of Things, Next Generation Networks and Cloud Computing"
Foundation of Computer Science USA
ICINC2017 - Number 1
July 2018
Authors: Namrata R. Bhamre, Nitin N. Patil
e1a7529b-996f-4780-9137-89409d160765

Namrata R. Bhamre, Nitin N. Patil . User Personalization based Product Ranking using Sentimental Reviews. International Conference on “Internet of Things, Next Generation Networks and Cloud Computing". ICINC2017, 1 (July 2018), 32-38.

@article{
author = { Namrata R. Bhamre, Nitin N. Patil },
title = { User Personalization based Product Ranking using Sentimental Reviews },
journal = { International Conference on “Internet of Things, Next Generation Networks and Cloud Computing" },
issue_date = { July 2018 },
volume = { ICINC2017 },
number = { 1 },
month = { July },
year = { 2018 },
issn = 0975-8887,
pages = { 32-38 },
numpages = 7,
url = { /proceedings/icinc2017/number1/29673-1749/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on “Internet of Things, Next Generation Networks and Cloud Computing"
%A Namrata R. Bhamre
%A Nitin N. Patil
%T User Personalization based Product Ranking using Sentimental Reviews
%J International Conference on “Internet of Things, Next Generation Networks and Cloud Computing"
%@ 0975-8887
%V ICINC2017
%N 1
%P 32-38
%D 2018
%I International Journal of Computer Applications
Abstract

The internet has dramatically changed the life of people by creating a new way for expressing their views and opinions on products of various domains. There are millions of products sold online by various merchants every year. These merchants allow their consumers to post their reviews on different products. The consumer reviews contribute to obtain quality information of the products. There is huge collection of consumer reviews are now available on the internet. This makes it difficult for the consumers to take wise decision on purchasing product. The consumer reviews are disorganized in nature due to that it creates difficulties in information navigation and knowledge acquisition in product reviews. In this case, the aspect gives the clear vision on the quality of the product. The existing methods are used to identify and rank product aspects automatically based on their weights and aspect rating. In this paper, we have personalized aspect ranking of the product by taking personnel preferences of the consumers. The proposed modified user personalization based product ranking approach solves the problem of meaningless reviews. This approach allows consumers to post reviews in the form of text review, sentimental review and product rating. The experimental results confirm that the proposed modified technique is highly effective. It outperforms the existing methods consequently.

References
  1. Zheng-Jun Zha, Jianxing Yu, Jinhui Tang, Meng Wang, and Tat-Seng Chua, "Product Aspect Ranking ad Its Applications", in proc. IEEE Transactions On Knowledge And Data Engineering, Vol. 26, No. 5, May 2014.
  2. Yuanbin Wu, Qi Zhang, Xuanjing Huang, and Lide Wu, "Phrase Dependency Parsing for Opinion Mining", in proc. ACL, Singapore, pp. 1533-1541, 2009.
  3. Bruno Ohana and Brendan Tierney, "Sentiment Classification of Reviews using SentiWordNet", in proc. IT&T Conf. , Dublin, Ireland, 2009.
  4. J. Yu, Z. -J. Zha, M. Wang, and T. S. Chua, "Aspect ranking: Identifying important product aspects from online consumer reviews," in Proc. ACL, Portland, OR, USA, pp. 1496–1505,2011.
  5. Ana-Maria Popescu and Oren Etzioni, "Extracting Product Features and Opinions from Reviews", in proc. HLT/EMNLP, Vancouver, BC, Canada, pp. 339-346, 2005
  6. Xiaowen Ding, Bing Liu, and Philip S. Yu, "A Holistic Lexicon- Based Approach to Opinion Mining", in proc. WSDM, New York, NY, USA, pp. 231-240, 2008.
  7. Vishal Gupta and Gurpreet Singh Lehal, "A Survey of Text Summarization Extraction Techniques", in proc. J. Emerg. Technol. Web Intell, vol. 2, No. 3, pp. 258-268, August 2010.
  8. Minqing Hu and Bing Liu, "Mining and Summarizing Customer Reviews", in proc. SIGKDD, Seattle, WA, USA, pp. 168-177, 2004.
  9. Bing Liu, "Sentiment Analysis and Subjectivity", in a handbook of natural language processing, New York, NY, USA: Marcel Dekker, Inc. , 2009.
  10. Bo Pang, Lillian Lee, and S. Vaithyanathan, "Thumbs up? Sentiment Classification using Machine Learning Techniques", in proc. EMNLP, Philadelphia, PA, USA, pp. 79-86, 2002.
  11. Bing Liu, Minqing Hu, and Junsheng Cheng, "Opinion Observer: Analyzing and Comparing Opinions on the Web", in proc. 14th Int. Conf. WWW, China, Japan, pp. 342-351, 2005.
  12. T. Wilson, J. Wiebe, and P. Hoffmann, "Recognizing contextual polarity in phrase-level sentiment analysis," in Proc. HLT/EMNLP, Vancouver, BC,Canada,pp. 347–354,2005.
  13. Rutuja Tikait, Ranjana Badre and Mayura Kinikar, "Product Aspect Identification and Ranking System", in proc. of International Journal of Science, Engineering and Technology Research (IJSETR), Volume 4, Issue 4, April 2015.
  14. Dhanashri Londhe, "Product aspect ranking on the consumers reviews and its applications", in proc. of International Journal of Advanced Research in Computer and Communication Engineering, Vol. 5, Issue 7, July 2016.
Index Terms

Computer Science
Information Sciences

Keywords

Consumer Reviews Product Aspects Sentiments Aspect Rating Sentimental Reviews.