Green Marketing Analysis for significant Investment Utilizing NLP Process

Machine learning techniques grounded on mathematical linguistics, such as Natural Language Processing, may detect shifts in public sentiment. This article explores the potential of using Latent Dirichlet Allocation (LDA) and natural language processing (NLP) methods to gauge the public’s rising concern for ecological and conservation issues. It will serve as the foundation upon which factories may base their eco-marketing choices. Massive expenditures are needed to refocus manufacturing on green commodities creation, market introduction of new ecological products, and promotion of energy-saving technology. Capturing the consistent demand from contractors and customers for decolonization is essential for generating a profit. The article contrasts the traditional data collection techniques (such as surveys) with the more modern machine learning techniques. Popularity, media coverage, and the variety of people actively engaged in the discussion are used to determine the importance of various data sources. The World Health Organization (WHO) has made the connection between pollution in the environment and deteriorating health. The author has identified foundational classes and terms that shape how people think about environmental marketing. This report uses NLP to gauge how people feel about ecological marketing concepts.