![]() summary - summary of the review (description).įor the algorithm, we’d only need two: the review corpus and its rating.reviewerName - the name of the reviewer.reviewerID - ID of the reviewer, like A3SPTOKDG7WBLN.reviewText - text of the review (heading).helpful - helpfulness rating of the review - example: 2/3.asin - ID of the product, like B000FA64PK.The structure of the csv file includes 10 columns: If you want to review what sentiment analysis is, I can suggest a quick read of this article, which covers all the basics. The goal would be to produce a high-performing sentiment analyzer by training it on one portion of the dataset. The dataset, which you can find at the following link, is composed of roughly 1,000,000 book reviews. Although SVM is a great algorithm for classification problems, is it also the best choice? With this new project, the goal now is to include the model selection mechanism.īy pipelining multiple models we can compare them on different aspects, including accuracy and efficiency. In a previous article, we optimized a Support Vector Machines algorithm on an IMDB movie review database. This article aims at selecting and deploying the optimal machine learning model to perform sentiment analysis on a dataset of book reviews from the Amazon Kindle Store. Photo by Emil Widlund on Unsplash Introduction
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