Review Analysis: an Approach to Leveraging User-Generated Content in the Context of Retail: What are customers really thinking? What are they looking for specifically when shopping for a product? And if they are satisfied with their purchase, what is the main reason?
Today’s technology offers many different avenues for customers to express themselves, set their expectations in writing, and share their opinion, frustration or satisfaction regarding all kinds of products and services. ‘Leaving a review’ has become an integral part of the purchase process. Through reviews, customers are volunteering invaluable information that can be turned into insights that would help drive business decisions (if you are a retailer), or help you make a successful purchase (if you are a customer). Yet the amount of data available to make these decisions is oftentimes extremely large, and it might be difficult for a human to read and synthesize all that has been said about their product of interest.
Review analysis and opinion mining offer solutions to automate the analysis of customer feedback through large-scale machine learning, natural language processing and sentiment analysis, and allow retailers to better understand their customers… as well as their data.
In this talk, I will present the various ways in which machine learning techniques can be used to extract the most significant features for a given category of products. I will then dig into a process aiming at identifying the sentiments relative to these features, and a useful way to aggregate this information into insights that are both usable and readable by any user. I will end with mentions to some of the challenges met when trying to extract objective information from a data source likely tainted with human subjectivity in an ever-changing market.