Mining Changes in User Expectation Over Time From Online Reviews
- URL: http://arxiv.org/abs/2001.09898v1
- Date: Mon, 13 Jan 2020 12:57:06 GMT
- Title: Mining Changes in User Expectation Over Time From Online Reviews
- Authors: Tianjun Hou (LGI), Bernard Yannou (LGI), Yann Leroy, Emilie Poirson
(IRCCyN)
- Abstract summary: We propose an approach for capturing changes of user expectation on product affordances based on the online reviews for two generations of products.
First, the approach uses a rule-based natural language processing method to automatically identify and structure product affordances from review text.
Finally, changes of user expectation can be found by applying the conjoint analysis on the online reviews posted for two successive generations of products.
- Score: 0.7742297876120561
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Customers post online reviews at any time. With the timestamp of online
reviews, they can be regarded as a flow of information. With this
characteristic, designers can capture the changes in customer feedback to help
set up product improvement strategies. Here we propose an approach for
capturing changes of user expectation on product affordances based on the
online reviews for two generations of products. First, the approach uses a
rule-based natural language processing method to automatically identify and
structure product affordances from review text. Then, inspired by the Kano
model which classifies preferences of product attributes in five categories,
conjoint analysis is used to quantitatively categorize the structured
affordances. Finally, changes of user expectation can be found by applying the
conjoint analysis on the online reviews posted for two successive generations
of products. A case study based on the online reviews of Kindle e-readers
downloaded from amazon.com shows that designers can use our proposed approach
to evaluate their product improvement strategies for previous products and
develop new product improvement strategies for future products.
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