Sustainable E-Commerce Through Customer Emotion Recognition with Machine Learning

As the e-commerce industry develops, companies seeking to satisfy customers and meet environmental targets throughout the world are turning to sustainability as a major motivator. To improve sustainable e-commerce practices, this study investigates the combination of machine learning (ML) with emotion identification. This paper reflects all sustainable customer buying behaviour by adopting the lifestyle, such as luxury, wealth, uniqueness, trendiness, and environmental consciousness. This paper puts forth a unique framework that makes use of innovative machine learning algorithms to assess client emotions in real-time, allowing e-commerce platforms to customize experiences that satisfy customers' demands while also encouraging sustainable behaviour. Businesses may minimize their influence on the environment, maximize customer retention, enhance product suggestions, and establish individualized and ethical marketing tactics by identifying emotions. The design of the suggested system, implementation difficulties, and the possible advantages of emotional recognition in promoting a long-lasting e-commerce ecosystem are all covered in our study. We show how emotional data may be a potent tool in promoting sustainable consumption habits and lowering the carbon footprint of online purchasing activities when used ethically and skilfully. By highlighting the relationship between emotion detection, machine learning, and e-commerce, this research adds to the expanding subject of sustainable technology and provides valuable insights for both academic and industrial audiences.