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Mining millions of reviews: a technique to rank products based on importance of reviews

Published: 03 August 2011 Publication History

Abstract

As online shopping becomes increasingly more popular, many shopping web sites encourage existing customers to add reviews of products purchased. These reviews make an impact on the purchasing decisions of potential customers. At Amazon.com for instance, some products receive hundreds of reviews. It is overwhelming and time restrictive for most customers to read, comprehend and make decisions based on all of these reviews. Customers most likely end up reading only a small fraction of the reviews usually in the order which they are presented on the product page. Incorporating various product review factors, such as: content related to product quality, time of the review, content related to product durability and historically older positive customer reviews will have different impacts on the products rankings. Thus, the automated mining of product reviews and opinions to produce a re-calculated product ranking score is a valuable tool which would allow potential customers to make more informed decisions. In this paper, we present a product ranking model that applies weights to product review factors to calculate a products ranking score. Our experiments use the customer reviews from Amazon.com as input to our product ranking model which produces product ranking results that closely relate to the products sales ranking as reported by the retailer.

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cover image ACM Other conferences
ICEC '11: Proceedings of the 13th International Conference on Electronic Commerce
August 2011
261 pages
ISBN:9781450314282
DOI:10.1145/2378104
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Published: 03 August 2011

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ICEC '11
ICEC '11: 13th International Conference on Electronic Commerce
August 3 - 5, 2011
Liverpool, United Kingdom

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Overall Acceptance Rate 150 of 244 submissions, 61%

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Cited By

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  • (2024)Effectiveness of ELMo embeddings, and semantic models in predicting review helpfulnessIntelligent Data Analysis10.3233/IDA-23034928:4(1045-1065)Online publication date: 17-Jul-2024
  • (2024)TechnoSearch: Improving e-Commerce Searches Using Product Category and Brand Based Ranking2024 International Conference on Electrical, Communication and Computer Engineering (ICECCE)10.1109/ICECCE63537.2024.10823605(1-6)Online publication date: 30-Oct-2024
  • (2023)Using Topic Modeling for Extracting Customers’ Expectations: A Case of Women ApparelBusiness Perspectives and Research10.1177/22785337221150831(227853372211508)Online publication date: 28-Apr-2023
  • (2023)Sentiment Analysis of Product Reviews by using Naive Bayes and Vader Models2023 5th International Conference on Smart Systems and Inventive Technology (ICSSIT)10.1109/ICSSIT55814.2023.10061002(1-6)Online publication date: 23-Jan-2023
  • (2023)Towards Comparable Ratings: Quantifying Evaluative Phrases in Physician ReviewsData Management Technologies and Applications10.1007/978-3-031-37890-4_3(45-65)Online publication date: 23-Jul-2023
  • (2022)EAPRAST: Extensive Approach for Product Ranking in Aspect-Based Sentiment Analysis using TRIEInternational Journal of Innovative Technology and Exploring Engineering10.35940/ijitee.C9762.011132211:3(51-58)Online publication date: 30-Jan-2022
  • (2022)Product selection based on sentiment analysis of online reviews: an intuitionistic fuzzy TODIM methodComplex & Intelligent Systems10.1007/s40747-022-00678-wOnline publication date: 20-Feb-2022
  • (2022)Personalized ranking of products using aspect-based sentiment analysis and Plithogenic setsMultimedia Tools and Applications10.1007/s11042-022-13315-y82:1(1261-1287)Online publication date: 15-Jun-2022
  • (2022)Review-Based Recommender System for Hedonic and Utilitarian Products in IoT FrameworkIoT as a Service10.1007/978-3-030-95987-6_16(221-232)Online publication date: 8-Jul-2022
  • (2021)Customer Review Analysis: A Systematic Review2021 IEEE/ACIS 6th International Conference on Big Data, Cloud Computing, and Data Science (BCD)10.1109/BCD51206.2021.9581965(91-97)Online publication date: 13-Sep-2021
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