Our aim is to understand reviews from the point of view of the arguments they contain, and then do a first step from how arguments are distributed in such reviews towards the behaviour of the reviewers that posted them. We consider 253 reviews of a selected product (a ballet tutu for kids), extracted from the “Clothing, Shoes and Jeweller” section of Amazon.com. We explode these reviews into arguments, and we study how their characteristics, e.g., the distribution of positive (in favour of purchase) and negative ones (against purchase), change through a period of four years. Among other results, we discover that negative arguments tend to permeate also positive reviews. As a second step, by using such observations and distributions, we successfully replicate the reviewers’ behaviour by simulating the review-posting process from their basic components, i.e., the arguments themselves.
Gabbriellini, S., Santini, F., From reviews to arguments and from arguments back to reviewers’ behaviour, Contributed paper, in Agents and Artificial Intelligence, (Roma, 24-26 February 2016), Springer Verlag, GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND 2017:<<LECTURE NOTES IN COMPUTER SCIENCE>>,10162 56-72. 10.1007/978-3-319-53354-4_4 [https://hdl.handle.net/10807/299897]
From reviews to arguments and from arguments back to reviewers’ behaviour
Gabbriellini, SimonePrimo
Conceptualization
;
2017
Abstract
Our aim is to understand reviews from the point of view of the arguments they contain, and then do a first step from how arguments are distributed in such reviews towards the behaviour of the reviewers that posted them. We consider 253 reviews of a selected product (a ballet tutu for kids), extracted from the “Clothing, Shoes and Jeweller” section of Amazon.com. We explode these reviews into arguments, and we study how their characteristics, e.g., the distribution of positive (in favour of purchase) and negative ones (against purchase), change through a period of four years. Among other results, we discover that negative arguments tend to permeate also positive reviews. As a second step, by using such observations and distributions, we successfully replicate the reviewers’ behaviour by simulating the review-posting process from their basic components, i.e., the arguments themselves.
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