Sentiment and Trend Analysis in Time Stamped Chats using ML for Employee Insights

Understanding emotional expressions in digital conversations is crucial in today’s communication landscape. This research focuses on sentiment and trend analysis in timestamped chats using machine learning to extract valuable employee insights. The proposed model integrates Natural Language Processing (NLP), Large Language Models (LLMs), and sentiment analysis techniques to identify emotional shifts over time. By leveraging real-time data processing and visualization, this study provides actionable insights for businesses to enhance employee engagement and mental well-being. Our approach is designed to detect emotional patterns and trends, assisting organizations in improving workplace communication. The results demonstrate the model’s capability to track emotional dynamics, offering a novel perspective on digital communication analytics.