Refine your search
Collections
Co-Authors
Journals
Year
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Kawate, Sumit
- An Approach for Reviewing and Ranking the Customers' Reviews through Quality of Review (QoR)
Abstract Views :260 |
PDF Views:4
Authors
Sumit Kawate
1,
Kailas Patil
1
Affiliations
1 Department of Computer Engineering, Vishwakarma Institute of Information Technology, IN
1 Department of Computer Engineering, Vishwakarma Institute of Information Technology, IN
Source
ICTACT Journal on Soft Computing, Vol 7, No 2 (2017), Pagination: 1390-1401Abstract
Quality is referred as the degree of excellence that means the expected product or service being considered to achieve desired requirements. Whereas, Quality of Reviews (QoR) relates to the task of determining the quality, efficiency, suitability, or utility of each review by addressing Quality of Product (QoP) and Quality of Service (QoS). It is an essential task of ranking, the reviews based on the quality and efficiency of the reviews given by the users. Whatever the reviews are provided to the particular product or services are from user experiences. The Quality of Reviews (QoR) is one of a kind method that defines how the customer's standpoint for the service or product that he/she experienced. The main issue while reviewing any product, the reviewer provides his/her opinion in the form of reviews and might be a few of those reviews are malicious spam entries to skew the rating of the product. Also in another case, many times customers provide the reviews which are quite common and that won't helpful for the buyer to interpret the helpful feedback on their products because of too many formal reviews from distinct customers. Hence, we proposed novel approaches: i) to statistical analyzes the customer reviews on products by Amazon to identify top most useful or helpful reviewers; ii) to analyze the products and its reviews associated for malicious reviews ratings that skewed the overall product ranking. As this is one of the efficient approaches to avoid spam reviewers somehow from reviewing the products. With this, we can use this method for distinguishing between nominal users and spammers. This method helps to quest for helpful reviewers not only to make the product better from best quality reviewers, but also these quality reviewers themselves can able to review future products.Keywords
Quality of Review, Opinion Mining, Sentiment Analysis, Quality of Product, Quality of Experience.
References
- Bipin Upadhyaya, Ying Zou, Iman Keivanloo and Joanna Ng, “Quality of Experience: User’s Perception about Web Services”, IEEE Transactions on Services Computing, Vol. 8, No. 3, pp. 410-421, 2015.
- Jo Mackiewicz and Dave Yeats, “Product Review Users Perceptions of Review Quality: The Role of Credibility, Informativeness, and Readability”, IEEE Transactions on Professional Communication, Vol. 57, No. 4, pp. 309-324, 2014.
- Julian McAuley and Jure Leskovec, “Hidden Factors and Hidden Topics: Understanding Rating Dimensions with Review Text”, Proceedings of the 7th ACM Conference on Recommender Systems, pp. 165-172, 2013.
- Luisa Mich, “Evaluating Website Quality by Addressing Quality Gaps: A Modular Process”, IEEE International Conference on Software Science, Technology and Engineering, pp. 42-49, 2014.
- Noi Sian Koh, Nan Hu and Eric. K. Clemons, “Do Online Reviews Reflect a Product's True Perceived Quality?-An Investigation of Online Movie Reviews Across Cultures”, Proceedings of 43rd Hawaii International Conference on System Sciences, pp. 374-385, 2010.
- Oren Tsur and Ari Rappoport. “RevRank: A Fully Unsupervised Algorithm for Selecting the Most Helpful Book Reviews”, Proceedings of 3rd International Conference on Weblogs and Social Media, pp. 154-161, 2009.
- Walisa Romsaiyud and Wichian Premchaiswadi, “Exploring Web Search Behavior Patterns to Personalize the Search Results”, Proceedings of 3rd International Conference on Intelligent Networking and Collaborative Systems, pp. 313-319, 2011.
- A. Malibari Almagrabi and J. McNaught. “A Survey of Quality Prediction of Product Reviews”, International Journal of Advanced Computer Science and Applications, Vol. 6, No. 11, pp. 49-58, 2015.
- S.S. Wani and Y.N. Patil, “Analysis of Data Retrieval and Opinion Mining System”, Proceedings of IEEE Conference Advance Computing Conference, pp. 681-685, 2015.
- W. Wang and Y. Zhou, “E-business Websites Evaluation based on Opinion Mining”, Proceedings of International Conference on Electronic Commerce and Business Intelligence, pp. 87-90, 2009.
- Q. Lu and C.G. Zhang, “The Study and Application of E-Business Website based on the Web Data Mining”, Advanced Materials Research, Vol. 846-847, pp. 1431-1434, 2013.
- Z. Zhang, J. Qi and G. Zhu, “Mining Customer Requirement from Helpful Online Reviews”, Proceedings of Enterprise Systems Conference, pp. 249-254, 2014.
- P.V. Rajeev and V.S. Rekha, “Recommending Products to Customers using Opinion Mining of Online Product Reviews and Features”, International Conference on Circuit, Power and Computing Technologies, pp. 1-5, 2015.
- Y. Zhang and D. Zhang, “Automatically Predicting the Helpfulness of Online Reviews”, Proceedings of IEEE 15th International Conference on Information Reuse and Integration, pp. 662-668, 2014.
- Yuanlin Chen, Yueting Chai, Yi Liu and Yang Xu, “Analysis of Review Helpfulness based on Consumer Perspective”, Tsinghua Science and Technology, Vol. 20, No. 3, pp. 293-305, 2015.
- Vishal Meshram, Vidula Meshram and Kailas Patil, “A Survey on Ubiquitous Computing”, ICTACT Journal on Soft Computing, Vol. 6, No. 2, pp. 1130-1135, 2016.
- Kailas Patil, Xinshu Dong, Xiaolei Li, Zhenkai Liang and Xuxian Jiang, “Towards Fine-Grained Access Control in Javascript Contexts”, Proceedings of 31st International Conference on Distributed Computing Systems, pp. 720-729, 2011.
- Kailas Patil and Braun Frederik, “A Measurement Study of the Content Security Policy on Real-World Applications”, International Journal of Network Security, Vol. 18, No.2, pp. 383-392, 2016.
- Kailas Patil, Tanvi Vyas, Frederik Braun, Mark Goodwin and Zhenkai Liang, “Poster: Usercsp-User Specified Content Security Policies”, Proceedings of Symposium on Usable Privacy and Security, pp. 1-2, 2013.
- S. Wang, Z. Zheng, Z. Wu, M.R. Lyu and F. Yang, “Reputation Measurement and Malicious Feedback Rating Prevention in Web Service Recommendation Systems”, IEEE Transactions on Services Computing, Vol. 8, No. 5, pp. 755-767, 2015.
- Siddu P. Algur and Jyothi G. Biradar, “Rating Consistency and Review Content Based Multiple Stores Review Spam Detection”, Proceedings of International Conference on Information Processing, pp. 685-690, 2015.
- Shangguang Wang, Zibin Zheng, Qibo Sun, Hua Zou and Fangchun Yang, “Evaluating Feedback Ratings for Measuring Reputation of Web Services”, Proceedings of IEEE International Conference on Services Computing, pp. 192-199, 2011.
- Meng Qiu, Liang He and Jing Xue, “A Model for Feedback Credibility of Trust Management in Web Services”, Proceedings of International Seminar on Future Information Technology and Management Engineering, pp. 201-205, 2008.
- S. Xie, Q. Hu, J. Zhang and P.S. Yu, “An Effective and Economic Bi-Level Approach to Ranking and Rating Spam Detection”, Proceedings of IEEE International Conference on Data Science and Advanced Analytics, pp. 1-10, 2015.
- Nitin Jindal and Bing Liu, “Analyzing and Detecting Review Spam”, Proceedings of 7th IEEE International Conference on Data Mining, pp. 547-552, 2007.
- Haoran Xu and Yuqing Sun, “Identify User Variants based on User Behavior on Social Media”, Proceedings of IEEE 34th International Conference on Performance Computing and Communications, pp. 1-8, 2015
- Denil D. Kotzias, Misha Denil, Nando De Freitas and Padhraic Smyth, “From Group to Individual Labels using Deep Features”, Proceedings of the 21th ACM International Conference on Knowledge Discovery and Data Mining, pp. 597-606, 2015.
- Abhijeet S. Kurle and Kailas R. Patil, “Survey on Privacy Preserving Mobile Health Monitoring System using Cloud Computing”, International Journal of Electrical, Electronics and Computer Science Engineering, Vol. 3, No. 4, pp. 31-36, 2015.
- Samay S Omanwar, Kailas Patil and Narendra P Pathak, “Flexible and Fine-Grained Optimal Network Bandwidth Utilization using Client Side Policy”, International Journal of Scientific and Engineering Research, Vol. 6, No. 7, pp. 692-698, 2015.
- Dnyaneshwar K Patil and Kailas Patil, “Automated Client-side Sanitizer for Code Injection Attacks”, International Journal of Information Technology and Computer Science, Vol. 8, No. 4, pp. 86-95, 2016.
- Kailas Patil, “Preventing Click Event Hijacking by User Intention Inference”, ICTACT Journal on Communication Technology, Vol. 7, No. 4, pp. 1408-1416, 2016.
- Ronak Shah and Kailas Patil, “Evaluating Effectiveness of Mobile Browser Security Warnings”, ICTACT Journal on Communication Technology, Vol. 7, No. 3, pp. 1373- 1378, 2016.
- Qaidjohar Jawadwala and Kailas Patil, “Design of a Novel Lightweight Key Establishment Mechanism for Smart Home Systems”, Proceedings of 11th IEEE International Conference on Industrial and Information Systems, 2016.