NLP based Model for Classification of Complaints Autonomous and Intelligent System

Abstract

These days, Artificial Intelligence is playing a key role in the progression of humanity as it helps to curtail human struggle in every aspect of life. An Immense amount of data is in structured and non-structured foam from numerous industrial platforms that are striving to get into the shape of useful information to be a part of scientific research. Although today’s major concern is how to manage a huge amount of feedback data i.e., Text format citizen complaints. At this point, proposing a model that automatically classifies the textual complaints by analyzing the content with the help of NLP (Natural Language Processing) and different ML (machine learning) models can be beneficial. Primarily, data of complaints are collected from the concerned platforms as well as from the international Consumer Complaint Database (for validation). The methodology is comprised of four different stages i.e. (1) initial pre-processing (2) preprocessing (3) future extraction (a) count vectorizer (b) term frequency-inverse document frequency (TF-IDF) (4) ML models for categorical classification of the complaints. At the evaluation stage, 10 different classes are present in assembled complaint dataset and more than 70 % accuracy is achieved from all classifiers. Likewise, on Consumer Complaint Dataset, 86% accuracy has been achieved. This model is used to optimize the complaint division automatically and saves a lot of time.