Amazon Reviews Sentiment Analysis, Segmentation, Classification and Prediction leveraging Multi-Class Multi-Output Classification
Keywords:
Amazon Reviews, Segmentation, Opinion Mining, Sentiment Analysis, Machine Learning ClassificationAbstract
Most users provide their reviews on the assorted products on the Amazon website. The reviews provided by users are most often compact. Hence it becomes a loaded source for sentiment analysis. The Sentiment Analysis is a substantially employed method for locating and obtaining the appropriate polarity of text sources. This project centers on a contrastive study of machine learning techniques for classifying the emotions of the considered product reviews dataset into Positive polarity, Neutral polarity, and Negative polarity, segment into Product, Delivery, Packaging categories. This can be helpful for consumers who want to look at the reviews of products before purchase and for companies who wish to look at the public’s reaction to their products. In this project, we correlate the performance of algorithms which support multi class multi output classifications, the accuracy of classifying the sentiment of an unknown review, insight on sentiment analysis, segmentation, and therefore comparison of the performance of the considered algorithms for the classification of the sentiments supported by several performance metrics.
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