Mining Changes in User Expectation Over Time From Online Reviews
This work addresses the need for designers to adapt products based on evolving customer feedback, though it is incremental as it applies existing methods like NLP and conjoint analysis to a new context of online reviews.
The authors tackled the problem of tracking shifts in user expectations over time by analyzing online reviews of successive product generations, using a rule-based NLP method to extract product affordances and conjoint analysis to categorize them, with a case study on Kindle e-readers demonstrating its utility for evaluating and developing product improvement strategies.
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.