Explainable Sentiment Analysis for Textile Personalized Marketing

Abstract

According to the United Nations (UN), the 2030 Agenda recognizes ending poverty in all of its forms and dimensions, including severe poverty which is the biggest global challenge and a necessity for sustainable development. Sustainable Development Goal (SDG) 1 aims to “end poverty in all its forms everywhere” but a global uniform approach has not been found rather than country-specific programs that tackle poverty with international initiatives to support them toward the SDG poverty eradication goal. With the opportunities generated by the 4th Industrial Revolution, nations can optimize their economic strengths to end poverty. Since the textile industry is one of the economic power engines of many developing countries, optimal strategic enhancements can feasibly ignite unexplored potentials with the emerging 4th Industrial Revolution technologies. With the rapid technological infrastructural developments, cloud computing has powered up lots of e-commerce businesses that enhance distribution of textile products. The problem is that there is no feedback channel for local and global customers to share their experience with the textile product developers and distributors, and this limits the marketing potentials of the textile producers and distributors locally and globally. In this work, we propose and demonstrate an explainable data-driven solution that optimizes 4th industrial Artificial Intelligence potentials and Natural Language Processing to boost textile product development and distribution. Our proposed Artificial Intelligence approach to sentiment analysis transparently explains evolving customer sentiments and preferences for textile products and provides insights for textile product development, personalized marketing, and distribution of textile garments for women.