Unveiling Sustainability in Ecommerce: GPT-Powered Software for Identifying Sustainable Product Features

Abstract

In recent years, the concept of sustainability has gained significant attention across various industries. Consumers are increasingly concerned about the environmental impact of the products they purchase, leading to a growing demand for sustainable options. However, identifying sustainable product features can be a complex and time-consuming task. This paper presents a novel approach to address this challenge by utilizing GPT (Generative Pre-trained Transformer) powered software for automatically identifying sustainable product features from product descriptions, titles, and product specifications. The software leverages the power of natural language processing and machine learning to classify products into different sustainability categories. By analyzing the textual information provided, the software can extract key sustainability indicators, such as eco-friendly materials, energy efficiency, recyclability, and ethical sourcing. This automated process eliminates the need for manual assessment and streamlines the evaluation of product sustainability. The proposed software not only empowers consumers to make informed and sustainable purchasing decisions but also facilitates businesses in showcasing their environmentally friendly offerings. The experimental results demonstrate the effectiveness and accuracy of the software in identifying sustainable product features. The primary objective of this article is to assess the suitability of the GPT model for the domain of sustainability assessment. By collecting a real-life dataset and employing a specific methodology, four hypotheses are formulated, which will be substantiated through the experimental outcomes. This research contributes to the field of sustainability assessment by combining advanced language models with product classification, paving the way for a more sustainable and eco-conscious future.

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“…Generative Pre-trained Transformer (GPT) models, built upon a foundational architecture, play a pivotal role in advancing NLP capabilities [33]. These models have significantly transformed the NLP landscape, demonstrating an exceptional ability to capture and comprehend intricate linguistic structures, context, and semantic nuances [34]. Through extensive pre-training on copious amounts of unlabeled textual data, GPT models gain a profound understanding of language patterns and relationships [30].…”