Gefilter Fish: Finding concise topics from Amazon’s customer reviews

But for some products, the sheer number of reviews overwhelms my ability to clearly identify how other customers felt about different features.Amazon’s “Read reviews that mention” feature is their attempt to summarize topics from reviews, but it has a few shortcomings: its topics are redundant and the reviewers’ overall opinions for each topic are unclear (more in the next section)..To improve Amazon’s “Read reviews that mention” feature, I developed the Chrome extension Gefilter Fish during my Insight Fellowship using Natural Language Processing techniques..For example, for this TV wall mount Amazon suggests seventeen words from customers’ reviews:Amazon’s “Read reviews that mention” feature with seventeen buttons for a TV wall mount.Amazon’s buttons are redundantMany of the buttons in this feature are redundant!.Clicking on the “install” button, it returned over five hundred reviews!It would be nice to know if the installation process is easy or difficult just at a glance, rather than needing to scan through several reviews to get that information!Gefilter Fish creates distinct & qualitative buttonsI created Gefilter Fish, a Chrome extension that replaces Amazon’s “Read reviews that mention” section with something more useful to help filter customer reviews:Gefilter Fish replaces Amazon’s “Read reviews that mention” with more concise buttons that include sentiment for a TV wall mount.The topics are much more concise (6 instead of the 17 in the original feature), and for each topic an emojis conveys the customers’ sentiment about that aspect of the product..Gefilter Fish’s 3 buttons, “install easy”, “mount wall tv”, and “stud plate screw” easily replace Amazon’s 11 buttons!Let’s talk about how I built this…The Gefilter Fish RecipeFishing for dataThe first step in any Data Science project is to get data and for Gefilter fish I needed lots of reviews for Amazon products..Unfortunately Amazon was quick to notice when I started scraping their pages for reviews, so the next best option was to use a static database of Amazon products with reviews.To create a static database of Amazon product reviews for Gefilter Fish, I leveraged Julian McAuley’s database of customer reviews..Customers can quickly glean that a product might be easy to install, but could also have bent studs or arrive broken.ValidationWith Gefilter Fish, I aimed to build a product that would reduce the number of topics from what Amazon’s “Read review that mention” feature provides..I had scraped words from Amazon’s “Read reviews that mention” feature for 400 products before Amazon started checking whether I was a robot..X represents the set of words output from Gefilter Fish and Amazon’s “Read reviews that mention” feature..I did the same thing for Amazon’s feature and then compared how well the two approaches performed across different products.Box plot distribution of the difference in redundancy between the topics from Amazon’s “Read reviews that mention” feature and Gefilter Fish..Amazon’s feature was more redundant than Gefilter Fish for most of the products (55%; positive values in the figure above).Instances where Gefilter Fish performed worse than Amazon (45%; negative values), can be explained by the fact that the redundancy metric in the above equation is really a lower bound for how well Gefilter Fish performs..It’s not a perfect metric, but it does give a rough idea of how well Gefilter Fish is doing.ConclusionI created Gefilter Fish as a Chrome extension to address problems I noticed in Amazon’s current feature for summarizing topics in reviews..Gefilter Fish reduces the number of topics by more 18% compared to Amazon’s original feature, while also providing a quick overview of customer sentiment .Gefilter Fish is something that Amazon could easily deploy in place of their existing feature.. More details

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