An Advanced AI-Driven Complaint Management System for RailMadad

With the growing number of passengers using Indian Railways, handling customer complaints efficiently has become a critical challenge. RailMadad, the official grievance redressal platform of Indian Railways, currently manages thousands of complaints daily, but categorizing, prioritizing, and routing these complaints manually often leads to delays. This study presents an AI-driven complaint management system designed to enhance the efficiency of RailMadad by leveraging Natural Language Processing (NLP) and Machine Learning (ML) techniques. The proposed system automatically classifies complaints based on predefined categories, assigns priority levels, and routes them to the relevant departments in real time. The system employs advanced algorithms such as BERT and RoBERTa for intent classification and sentiment analysis to assess the urgency of complaints, and the model is trained using publicly accessible datasets and complaint records. The system is very scalable and flexible for widespread use in Indian Railways, as evidenced by the experimental results, which show notable increases in customer satisfaction and complaint response time.

• Publication year

  2025

• Venue

  2025 5th International Conference on Evolutionary Computing and Mobile Sustainable Networks (ICECMSN)

• Publication date

  2025-11-24

• Fields of study

  Not labeled

• Identifiers
  DOI 10.1109/ICECMSN68058.2025.11382598
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• An Intelligent System for Classifying Patient Complaints Using Machine Learning and Natural Language Processing: Development and Validation Study

  2023cited by this paper
• FakeBERT: Fake news detection in social media with a BERT-based deep learning approach

  2021cited by this paper
• RoBERTa: A Robustly Optimized BERT Pretraining Approach

  2019cited by this paper
• A Innovative Topic Based Customer Complaints Sentiment classification System

  2019cited by this paper
• Know What You Don’t Know: Unanswerable Questions for SQuAD

  2018cited by this paper
• Attention is All you Need

  2017cited by this paper
• Hierarchical Attention Networks for Document Classification

  2016cited by this paper
• Very Deep Convolutional Networks for Large-Scale Image Recognition

  2014cited by this paper
• VADER: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text

  2014cited by this paper
• Neural Machine Translation by Jointly Learning to Align and Translate

  2014cited by this paper
• Adam: A Method for Stochastic Optimization

  2014cited by this paper
• Distributed Representations of Words and Phrases and their Compositionality

  2013cited by this paper
• Efficient Estimation of Word Representations in Vector Space

  2013cited by this paper
• Finding Deceptive Opinion Spam by Any Stretch of the Imagination

  2011cited by this paper
• Long Short-Term Memory

  1997cited by this paper

• No citing papers are available for this paper.