Exploring Sustainable Consumer Behavior Among Women Educators in West Cochin Using ML and Social Media Analytics

The study uses social media analytics and Machine Learning (ML) to examine sustainable consumer behavior among West Cochin women teachers. Particularly in sustainability, traditional approaches include polls and interviews suffer from temporal limits and biassed sample, therefore impeding real-time insights into consumer trends. Using natural language processing (NLP) for sentiment analysis and clustering for behavioral segmentation, the proposed system analyses enormous amounts of unstructured social media data using advanced ML techniques. The results show notable developments over existing systems: a sentiment analysis accuracy of 90.0%, clustering efficacy with a Silhouette score of 0.65, and predictive model performance with an accuracy of 85.0%. These results show the capacity of the proposed system to offer better understanding of sustainable customer behavior, so enabling focused marketing plans and a more environmentally friendly market.