Product reviews analysis to extract sentimental insights with class confidence rate using self-organizing map neural network

Abstract

Customer data analysis helps companies to understand customer intentions and behaviors better. This study introduces an analysis of product reviews to help managers adopt a more efficient strategy to extract valuable knowledge and help detect segment of customers that need a special attention and products that need improvement or with the most impact. The used dataset is a set of Amazon reviews divided into multiple categories; each review has a target column called ‘overall’ that takes a value between 1 and 5 (customer's satisfaction). Based on the ‘overall’ column, multiple labeling methods have been used and compared to get a binary target variable, positive or negative, that affects a class to a review. This dataset contains more than one million reviews and can give companies great insight into products’ quality and customers’ retention. This work has materialized by using customer segmentation and competitive learning with self-organizing map (SOM) Model and adopting a new approach to explore the generated network/map, it is based on clustering and map nodes labelling using a majority voting process. The results show that the proposed dual approach combining the prior knowledge, related to supervised learning, and the competitive learning abilities enhances the SOM model’s capabilities.