(apologies for cross-posting)
Title: Complementing Human Effort in Online Reviews: An Artificial Intelligence Approach to Automatic Content Generation and Review Synthesis
Abstract: Online product reviews are ubiquitous and helpful sources of information available to consumers for making purchase decisions. Consumers rely on both the quantitative aspects of reviews such as valence and volume as well as textual descriptions to learn about product quality and fit. In this paper we show how new achievements in natural language can provide an important assist for different kinds of review-related writing tasks. Working in the interesting context of wine reviews, we demonstrate that machines are capable of performing the critical marketing task of writing expert reviews directly from a fairly small amount of product attribute data (metadata). We conduct a kind of "Turing Test" to evaluate human response to our machine-written reviews and show strong support for the assertion that machines can write reviews that are indistinguishable from those written by experts. Rather than replacing the human review writer, we envision a workflow wherein machines take the metadata as inputs and generate a human readable review as a first draft of the review and thereby assist an expert reviewer in writing their review. We next modify and apply our machine-writing technology to show how machines can be used to write a synthesis of a set of product reviews. For this last application we work in the context of beer reviews (for which there is a large set of available reviews for each of a large number of products) and produce machine-written review syntheses that do a good job – measured again through human evaluation – of capturing the ideas expressed in the reviews of any given beer. For each of these applications, we adapt the Transformer neural net architecture. The work herein is broadly applicable in marketing, particularly in the context of online reviews. We close with suggestions for additional applications of our model and approach as well as other directions for future research.
Bio of the speaker:
Praveen Kopalle is the Signal Companies' Professor of Management and Professor of Marketing at the Tuck School of Business, Dartmouth College. Praveen was the Associate Dean for the MBA program at the Tuck School and the Chair of the marketing area at Tuck. Praveen received his Ph.D. from Columbia University, MBA from IIM, B.E. from Osmania University. Praveen's teaching and research interests include innovation, pricing, e-commerce, and machine learning. Professor Kopalle serves as an Associate Editor at the Journal of Marketing and Journal of Retailing and was an Associate Editor at the Journal of Consumer Research and International Journal of Research in Marketing. He currently serves or has served on the Editorial Boards of Journal of Marketing, International Journal of Research in Marketing, Journal of Retailing, Journal of Marketing Research, Marketing Science, Journal of Consumer Research, Marketing Letters, and Journal of Interactive Marketing. Professor Kopalle has won many awards including: Distinguished Alumni Award, Core Teaching Excellence Award at the Tuck School, John D. C. Little Best Paper Award, Best Paper Award on Marketing and Innovation, Finalist, John D. C. Little Best Paper Award, Finalist, Marketing Science Institute's Robert Buzzell award, William R. Davidson Award, American Marketing Association Consortium Faculty, Finalist, William R. Davidson Award, Visiting Scholar, RPI, Bocconi University, ISB, University of Texas.
Praveen published his research in top-tier journals including Journal of Consumer Research, Journal of Marketing Research, Marketing Science, Management Science, International Journal of Research in Marketing, Strategic Management Journal, JAMS, OBHDP, Journal of Retailing, Production and Operations Management, JPIM, Managerial and Decision Economics, Marketing Letters, Applied Economics, and IJEC. He has also been invited to speak at over sixty universities and institutes worldwide.
Registration link:
https://libcal.dartmouth.edu/calendar/itc/2022DSAIW1. Click or tap if you trust this link." data-linkindex="7">libcal.dartmouth.edu/calendar/itc/2022DSAIW1
** Registrants will receive a zoom link one day before the webinar.
Look forward to seeing you at the webinar!
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Jianjun Hua
Statistical Consultant
Dartmouth College
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