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...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. Reading the examples showed phrases commonly used in reviews such as "This is something I...", "It worked as expected", and "What more can I say?". So these types of clusters included less descript reviews that had common phrases. When modeling the data, I separated the reviews into 200 smaller groups (just over 8,000 reviews in each) and fit the model to each of those subsets. These types of common phrase groups were not very predictable in what words were emphasized.
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