A multi-criteria decision making approach for recommending a product using sentiment analysis

Nowadays, online platform has become a modern means of shopping among people. The reviews of products by customers have been proliferating on the online platform for a while. Since a large number of reviews are available, invariably customers read reviews before buying the product. Majority of the reviews are lengthy and repetitive, some of them even have nothing to do with the product itself. Going through the reviews before making a decision has become a tedious task. Further, the product selection is a complex decision making problem where several criteria are involved in the decision making process. Researchers have used methods like machine learning and sentiment classification to analyze the review of customers to summarize them. However, review summarization does not suggest the best/worst product. This study aims to recommend the best product based on the opinions expressed in the customers' reviews. We analyze the reviews of customers from various online platforms and use effective multi criteria decision making approach to evaluate and recommend the best suitable product. Real-time dataset from Flipkart and Amazon are used to evaluate our system's performance. Different case studies have shown that our proposed method produces a promising result which can help the user in the decision making process.

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