RANSFORMER-BASED AUTOMATION OF AUTHENTIC PRODUCT REVIEWS FOR SCALABLE SOCIAL PROOF IN E-COMMERCE

The rapid growth of e-commerce platforms has increased the pressure on a large quantity of genuine and interesting product reviews to create trust and stimulate buying behaviour. Manual review writing is time-consuming and resource-intensive, particularly when the size of the catalog goes into the thousands. New or niche products have a high likelihood of being discovered and not accepted by consumers due to insufficient reviews. Retailers and brands are in need of a solution to automatically create reviews that can replicate the variety and the believability of authentic customer reviews. An AI-generated review system with the transformer-based GPT-2 model that is capable of generating natural and believable product reviews. The automated system will enhance the e-commerce experience through offering scalable, authentic social proof on all product listings. The idea behind this project is to fill this gap between the existence of products and social proof through the use of sophisticated AI methods. With automation of review writing, businesses will be able to provide sufficient support to all product listings and eventually improve customer experience and increase the number of conversions. The study manages to automate the generation of natural product reviews by combining the newest deep learning and natural language processing technologies with the help of GPT-2. The results provided show that the model can capture imperceptible expressions of product functionality, quality, and satisfaction, all of which are critical in generating authentic reviews. The implications of this strategy for the e-commerce sites seeking scalable solutions to sentiment analysis, content development, and enhancement of customer engagement are significant. Another way to add to the work is the introduction of transformer-based architecture that preconditions further research.