“Harnessing Customer Feedback for Product Recommendations: An Aspect-Level Sentiment Analysis Framework”

Abstract

This research paper presents a novel approach for recommending products to customers based on their cared aspects by performing sentiment analysis on customer feedback. The proposed approach utilizes the WordNet database to identify and extract aspects from customer reviews and feedback, and then applies sentiment analysis techniques to determine the sentiment associated with each aspect. The resulting sentiment scores are then used to generate personalized product recommendations that align with the customer’s preferences and priorities. Here we extract the comments from an e-commerce website that is Amazon, and we then choose the most cared aspects from those comments. The dataset is publicly available online which contains reviews of each product. The chosen most cared aspects are price, colour, battery, and screen. These cared aspects are keywords that shopping online and recommending, will help to categorize the comments based on price, colour, battery, and screen. After categorizing the comments, it will be defined as the set of explicit comments. After an explicit comment set is defined, sentiment analysis is performed to systematically identify the interest of the customer through comments. Here the comments are classified into the polarity of given texts in an explicit comment set into positive, negative, and neutral. Finally, scores were calculated for all brands which will help to recommend the product.