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View Based Review Exploration

Published: 02 January 2021 Publication History

Abstract

In an e-commerce site, reviews help augment basic product descriptions with detailed user experiences and opinions on the product. These reviews greatly influence the purchase decisions of oncoming customers. Since these reviews are haphazardly presented, they call for befitting review exploration techniques. We propose a novel view based review exploration system that can be used to control the depth and content of the retrieved reviews. We propose three views depending on the depth personified by them: the Bird-eye view, the Unidirectional view, and the Microscopic view. The views present a ranked list of relevant reviews that meet the view criteria. The proposed view based exploration recorded precision as high as 0.85.

References

[1]
Christy MK Cheung, Matthew KO Lee, and Neil Rabjohn. 2008. The impact of electronic word-of-mouth: The adoption of online opinions in online customer communities. Internet research 18, 3 (2008), 229–247.
[2]
Prerna Chikersal, Soujanya Poria, and Erik Cambria. 2015. SeNTU: sentiment analysis of tweets by combining a rule-based classifier with supervised learning. In SemEval 2015. 647–651.
[3]
Alton YK Chua and Snehasish Banerjee. 2015. Understanding review helpfulness as a function of reviewer reputation, review rating, and review depth. Journal of the Association for Information Science and Technology 66, 2(2015), 354–362.
[4]
Cristian Danescu-Niculescu-Mizil, Gueorgi Kossinets, Jon Kleinberg, and Lillian Lee. 2009. How opinions are received by online communities: a case study on amazon. com helpfulness votes. In WWW. ACM, 141–150.
[5]
Günes Erkan and Dragomir R Radev. 2004. Lexrank: Graph-based lexical centrality as salience in text summarization. Journal of artificial intelligence research 22 (2004), 457–479.
[6]
Xing Fang and Justin Zhan. 2015. Sentiment analysis using product review data. Journal of Big Data 2, 1 (2015), 5.
[7]
Yihong Gong and Xin Liu. 2001. Generic text summarization using relevance measure and latent semantic analysis. In ACM SIGIR. ACM, 19–25.
[8]
Ruining He and Julian McAuley. 2016. Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering. In WWW. International World Wide Web Conferences Steering Committee, 507–517.
[9]
Nitin Jindal and Bing Liu. 2006. Identifying comparative sentences in text documents. In ACM SIGIR. ACM, 244–251.
[10]
Thorsten Joachims. 2006. Training linear SVMs in linear time. In ACM SIGKDD. ACM, 217–226.
[11]
Nikolaos Korfiatis, Elena GarcíA-Bariocanal, and Salvador Sánchez-Alonso. 2012. Evaluating content quality and helpfulness of online product reviews: The interplay of review helpfulness vs. review content. Electronic Commerce Research and Applications 11, 3(2012), 205–217.
[12]
Nanda Kumar and Izak Benbasat. 2006. Research note: the influence of recommendations and consumer reviews on evaluations of websites. Information Systems Research 17, 4 (2006), 425–439.
[13]
J Richard Landis and Gary G Koch. 1977. An application of hierarchical kappa-type statistics in the assessment of majority agreement among multiple observers. Biometrics (1977), 363–374.
[14]
Mengwen Liu, Yi Fang, Dae Hoon Park, Xiaohua Hu, and Zhengtao Yu. 2016. Retrieving Non-Redundant Questions to Summarize a Product Review. In ACM SIGIR. ACM, 385–394.
[15]
Yang Liu, Xiangji Huang, Aijun An, and Xiaohui Yu. 2008. Modeling and predicting the helpfulness of online reviews. In ICDM. IEEE, 443–452.
[16]
A Jenifer Jothi Mary and L Arockiam. 2017. ASFuL: Aspect based sentiment summarization using fuzzy logic. In ICAMMAET. IEEE, 1–5.
[17]
Rada Mihalcea and Paul Tarau. 2004. Textrank: Bringing order into text. In Proceedings of the 2004 conference on empirical methods in natural language processing. 404–411.
[18]
Susan M Mudambi and David Schuff. 2010. What makes a helpful review? A study of customer reviews on Amazon. com. (2010).
[19]
Jahna Otterbacher. 2009. ’Helpfulness’ in online communities: a measure of message quality. In SIGCHI. ACM, 955–964.
[20]
Maria Pontiki, Dimitris Galanis, Haris Papageorgiou, Ion Androutsopoulos, Suresh Manandhar, AL-Smadi Mohammad, Mahmoud Al-Ayyoub, Yanyan Zhao, Bing Qin, Orphée De Clercq, 2016. SemEval-2016 task 5: Aspect based sentiment analysis. In SemEval-2016. 19–30.
[21]
Mohammad Salehan and Dan J Kim. 2016. Predicting the performance of online consumer reviews: A sentiment mining approach to big data analytics. Decision Support Systems 81 (2016), 30–40.
[22]
Jiwei Tan, Xiaojun Wan, and Jianguo Xiao. 2015. Learning to Recommend Quotes for Writing. In AAAI. 2453–2459.
[23]
G Vinodhini and RM Chandrasekaran. 2017. A sampling based sentiment mining approach for e-commerce applications. Information Processing & Management 53, 1 (2017), 223–236.
[24]
Tao Wang, Yi Cai, Ho-fung Leung, Raymond YK Lau, Qing Li, and Huaqing Min. 2014. Product aspect extraction supervised with online domain knowledge. Knowledge-Based Systems 71 (2014), 86–100.

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CODS-COMAD '21: Proceedings of the 3rd ACM India Joint International Conference on Data Science & Management of Data (8th ACM IKDD CODS & 26th COMAD)
January 2021
453 pages
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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 02 January 2021

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Author Tags

  1. Information Search and Retrieval
  2. Learning to Rank
  3. Review Exploration

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  • Short-paper
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CODS COMAD 2021
CODS COMAD 2021: 8th ACM IKDD CODS and 26th COMAD
January 2 - 4, 2021
Bangalore, India

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Overall Acceptance Rate 197 of 680 submissions, 29%

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