Complaint Classification Model Using NLP

Abstract

This report discusses the research done on the chosen topic, which is ComplaintClassification Model using NLP. Artificial intelligence nowadays is playing a vital role in our society. It is just minimizing human labor and effort in every field. Industrial sector is feeding their large amount of structured and unstructured data to find out useful information for scientific research. The main alarming thing is how to operate the huge feedback data, which is in the form of complaints i.e., in text format. Here, we have proposed a model which automatically classifies the complaints by analyzing the text with the help of machine learning and NLP (Natural Language Processing) methods. We have initially collected a dataset from a portal containing complaints of citizens. For validation, we have also used another dataset of complaints from the Consumer Complaint Database. After tokenizing,stemming and lemmatization, different feature extraction techniques like count vectorizer and TF-IDF are used to convert all the textual data into numerical data. Then different machinelearning algorithms are used to classify the complaints into their categories. In ourgathered dataset, 10 different divisions for complaints are used and an accuracy of more than 70% is achieved with all classifiers. Similarly on the Consumer Complaint dataset, 86% accuracy has been achieved. The proposed model is helpful in saving a lot of time, as there is no need to go through each complaint and categorizing manually.

Introduction

Background:
Artificial Intelligence (AI), particularly Natural Language Processing (NLP), is widely used across industries to improve efficiency by processing large volumes of textual data, such as consumer complaints submitted via online portals or emails. Government and businesses receive vast feedback from citizens and customers, which is time-consuming to manually categorize. Automating complaint classification using NLP and machine learning can improve response times and accuracy.

Problem Statement:
Develop an automated system to classify consumer complaints to enhance customer service and operational efficiency.

Objective:
Create a machine learning model to automatically categorize consumer complaints into predefined groups (e.g., credit cards, loans, mortgages) to help organizations address issues quickly and improve customer support.

Literature Review:

• NLP & Machine Learning: Used to analyze and classify text data.

• Classical vs. Deep Learning Models: Classical models like SVM and TF-IDF are effective but limited; deep learning models (LSTM, Bi-LSTM, CNN) capture more complex language patterns.

• Word Embeddings: Techniques like Word2Vec, BERT improve semantic understanding.

• Large Language Models (LLMs): Advanced models like GPT-4 offer zero-shot classification capabilities.

• Sentiment Analysis & Topic Modeling: Provide insights into customer emotions and common issues.

Survey of Technologies:

• Classical ML Models: SVM, Random Forest, KNN used for text classification with varying pros and cons.

• Deep Learning Models: LSTM, Bi-LSTM, GRU, CNN enhance accuracy but require more data and compute power.

• Word Embeddings: Word2Vec, FastText, BERT, and DistilBERT capture semantic relations for better classification.

• LLMs: GPT-4 and reasoning models offer advanced NLP capabilities.

• Sentiment & Topic Modeling: Tools like TextBlob and LDA help analyze customer sentiment and identify key complaint themes.

Proposed System:
An automated NLP and machine learning system that preprocesses text (cleaning, tokenization, lemmatization), extracts features (TF-IDF), performs sentiment analysis, applies topic modeling (LDA), and classifies complaints using SVM and Random Forest. The system improves customer service by faster, more accurate complaint routing and insight generation.

Requirements:

• Hardware: Minimum 2GB RAM, 16GB disk, 1GB processor.

• Software: Windows OS, Python environment (Jupyter Notebook or VS Code), data visualization tools like Power BI.

Tool Selection Justification:
Python and Jupyter Notebook are chosen for their interactive computing, rich media integration, flexibility, extensive libraries (Pandas, Scikit-learn, NLTK, TextBlob), and community support.

System Design:
Includes use case diagrams showing system functionalities and actor interactions for gathering and managing requirements effectively.

Conclusion

The proposed model for classifying consumer complaints using NLP and machine learning demonstrates a robust and efficient approach to handling consumer grievances. By leveraging advanced text preprocessing techniques, feature extraction methods, sentiment analysis, topic modeling, and classification algorithms, the model achieves high accuracy in categorizing complaints into predefined categories. This automated system significantly reduces the manual effort required for complaint classification, enhances customer service response times, and provides valuable insights into common issues faced by consumers.

References

Bibliography: [1] Kulkarni, C.S., Bhavsar, A.U., Pingale, S.R. and Kumbhar, S.S, \"BANK CHAT BOT – An Intelligent Assistant System Using NLP and Machine Learning,\" International Research Journal of Engineering and Technology, vol. 04 , no. 05 , May -2017. [2] Tutika, A. and Nagesh, M.Y.V., \"Restaurant reviews classification using NLP Techniques,\" Journal of Information and Computational Science, vol. 9, no. 11, 2019. [3] Towfighi, S., Agarwal, A., Mak, D.Y. and Verma, A., \"Labelling chest x-ray reports using an open-source NLP and ML tool for text data binary classification,\" medRxiv, November 22, 2019. Websites: [1] https://www.kaggle.com/

Copyright

Copyright © 2025 Dipika Verma, Manikandan Thevar, Prof. Rani Mario. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.