AI-enabled identification of consumer’s purchase intention with a practical implementation through analysis of review comments in real time

The market has grown significantly in the digital age thanks to a variety of smart gadgets. E-commerce websites have become important marketplaces for buying and selling, successfully showcasing goods according to the requirements of their customers. To improve customer happiness and commercial outcomes, these platforms must continue to be able to comprehend and react in real-time to the feelings of their users, even in the face of their success. This research presents a unique model that generates real-time sentiment analysis of items based on user feedback from different geographical regions, with the goal of enhancing the capabilities of e-commerce platforms. The model analyzes user reviews and social media mentions using cutting-edge machine learning algorithms and natural language processing techniques to provide a precise indicator of how customers feel about certain items. E-commerce platforms are able to enhance their marketing techniques and provide more tailored recommendations as a result.Large databases of user-generated material are gathered and processed, pertinent sentiment indicators are found, and the outcomes are shown on the e-commerce platform as part of the suggested approach. Businesses may quickly identify changes in customer intents and preferences thanks to this real-time analysis. For example, if a product gets bad reviews from a certain location, the e-commerce platform may swiftly change its product offers or marketing strategies to solve the issues brought up by customers in that area.