AI-BASED INTELLIGENT AND EMOTION-AWARE GRIEVANCE REDRESSAL SYSTEM USING NLP AND MACHINE LEARNING

In conclusion, the proposed AI-based grievance redressal system successfully achieves its objective of improving the efficiency, accuracy, and responsiveness ofcomplaint management processes. The system leverages modern technologies such as machine learning, deep learning, and natural language processing to automate key tasks, including complaint classification, priority prediction, and sentiment analysis The experimental results demonstrate that the system performs effectively across various evaluation parameters. High accuracy in classification, efficient priority assignment, and reliable sentiment detection contribute to better handling of grievances. The system significantly reduces processing time from hours or days in traditional systems to just a few seconds, thereby ensuring faster resolution of issues

1.                Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. 2.                Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer. 3.                Goldberg, Y. (2017). Neural Network Methods for Natural Language Processing. Morgan & Claypool. 4.                Vaswani, A., et al. (2017). Attention Is All You Need. Proceedings of NeurIPS. 5.                Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. NAACL. 6.                Pedregosa, F., et al. (2011). Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research. 7.                Kim, Y. (2014). Convolutional Neural Networks for Sentence Classification. EMNLP. 8.                Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation.