AI-Driven Sentiment Analysis for Enhanced Predictive Maintenance and Customer Insights in Enterprise Systems

Abstract

Modern organizations and business environments are one step ahead of traditional ERP and CRM Enterprise systems to contribute to real-time analyses and interpretations of customers’ feedback for enhancing decision-making and maintenance mechanisms. These gaps are filled in this paper by proposing the incorporation of AI-based sentiment analysis into Enterprise systems and employing state-of-the-art models, VADER and RoBERTa. In this research, we offer a detailed process involving the integration of different components and data, a scrupulous data-gathering process, peculiar preprocessing measures, and optimal model deployment. Employing datasets from Kaggle, this research provides a proof-of-concept for utilizing artificial intelligence for sentiment analysis to improve predictive maintenance and customer understanding. The findings suggest that there is achievement of increased operations efficiency and hastened decision-making for a competitive edge in ERP systems and CRM systems for managing customers and prospects. The current research enriches the knowledge base by presenting a new concept to enhance Enterprise systems with AI and the potential of this move in the industry.