ONLINE JOURNAL OF ART AND DESIGN, cilt.10, sa.1, ss.245-261, 2022 (Hakemli Dergi)
In the era of Big Data, with the advances in e-commerce, users, rather than producers,
tend to pioneer to express to-be-improved product features with online product reviews.
Although there are many conventional methods for determining users' opinions about
available products, these methods are costly, non-voluntary, applied with a limited group,
and have the risk of including much bias. Reviewing user-generated product reviews has
distinct advantages over traditional methods. On the other hand, extracting high-value
data from online user reviews is challenging than interviews and market research. We
introduce a framework that helps extract useful data from online customer feedback using
accessible and handy tools to create pattern models in terms of clarification, comparability,
and validity. This article provides a business case which allows the decision-makers to
recognize the summarized and visualized review trends and their potential triggers that
could be considered for future product decisions