Harnessing the Power of Web Scraping and Machine Learning to Uncover Customer Empathy from Online Reviews

Abstract

In today's information-driven world, companies need to grasp customer sentiment and pinpoint key product features to make informed choices and improve customer satisfaction. The abundance of online customer feedback provides businesses with a rich source of information about customer perceptions and preferences. Yet manual analysis of large volumes of reviews is a tedious and resource-intensive task. It describes a web scraping application that sorts reviews into positive, negative, or neutral sentiments that businesses can use for insight. By assigning weights to reviews based on perceived impact, the approach offers a nuanced understanding of customer opinions. Experimental results demonstrate its effectiveness in accurately categorizing and analysing reviews, distinguishing between genuine and fake feedback. Additionally, AI-powered examination of product reviews employs NLP methods to extract important information from customer feedback, identifying particular elements that customers like or find troubling. After the successful identification of gain points, they can be used to improvise product development and marketing. So, product-specific focused crawler is developed to extract customer reviews from the most trusted websites. As a side chain, this can also be used as a tool for analysing the competitors’ products to understand their pain as well as gain points. The approach can uncover the most discussed aspects of products, enabling businesses to better understand customer perceptions and preferences to make better decisions thereby saving time and money.