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Other topics were more ambiguous. For example, one cluster had words such as: To create a model that can detect low-quality reviews, I obtained an Amazon review dataset on electronic products from UC San Diego. The dataset contains 1,689,188 reviews from 192,403 reviewers across 63,001 products. Most of the reviews are positive, with 60% of the ratings being 5-stars. hard, drive, data, speed, external, usb, files, fast, portable... = Hard Drives UCSD Dataset However, one cluster for generic reviews remained consistent between review groups that had the three most important factors being a high star rating, high polarity, high subjectivity, along with words such as perfect, great, love, excellent, product. The reviews from this topic, which I'll call the low-quality topic cluster, had exactly the qualities listed above that were expected for fake reviews. I used this as the target topic that would be used to find potential fake reviewers and products that may have used fake reviews. I spot checked many of these reviews, and did not see any that weren't a verified purchase. A likely explanation is that this person wants to write reviews, but is not willing to put in the time necessary to properly review all of these purchases. While this is consistent with a vast majority of his reviews, not all the reviews are 5-stars and the lower-rated reviews are more informative. It is likely that he just copy/pastes the phrase for products he didn't have a problem with, and then spends a little more time on the few products that didn't turn out to be good. online sports betting tennesseenfl football bettingyoutube, rumble & vimeo.