EDSIGCON Proceedings 2019

CONISAR Proceedings 2019

Cleveland, Ohio

Conference Highlights




2019 CONISAR Proceedings - Abstract Presentation


Which Amazon Reviews Should You Read?


Emre Gokce
University of North Carolina Wilmington

Douglas M Kline
University of North Carolina Wilmington

Ron Vetter
University of North Carolina Wilmington

Jeffrey Cummings
University of North Carolina Wilmington


Abstract
Customers have to make informed purchasing decisions and online reviews are often a tool customers use to help. However, if there are thousands of reviews, which ones should be read? We present a method of selecting a smaller subset of reviews that is similar to the set of all reviews. This is done through document-term frequency analysis which provides a matrix of term counts for each document (Salton & McGill, 1983) and optimization models including both linear and quadratic models with integer variables. The methods follow closely those found in Kline (1998).

We minimize the distance of the subset from the corpus subject review/word count constraints and also minimize the document/word count subject to distance of the subset from the corpus constraints. The technique is demonstrated on three products with 2000+ reviews from Amazon: a book, an article of clothing, and an electronic device. These products were selected based on each product having a different set (or corpus) of terms used in their reviews. This will test the proposed methods across varying corpora for generalizability.

References: Kline, D.M. (1998) Optimization Models for Creating Reduced Reading Lists. Working paper # 98-02MIS, Center for Business and Economic Development, Sam Houston State University, https://www.shsu.edu/centers/cbed/documents/working-papers/No.98-02MIS.pdf.
Salton, G. and McGill, M.J. (1983) Introduction to Modern Information Retrieval. McGraw-Hill Inc., New York, New York.

Recommended Citation: Gokce, E., Kline, D. M., Vetter, R., Cummings, J., (2019). Which Amazon Reviews Should You Read?. Proceedings of the Conference on Information Systems Applied Research, v.12 n.5252, Cleveland, Ohio