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Reseach Article

NLP based Grievance Redressal System

by Alok Pratap Singh, Ankur Goel, Aakansha Goel, Diksha Arya
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 184 - Number 12
Year of Publication: 2022
Authors: Alok Pratap Singh, Ankur Goel, Aakansha Goel, Diksha Arya
10.5120/ijca2022922104

Alok Pratap Singh, Ankur Goel, Aakansha Goel, Diksha Arya . NLP based Grievance Redressal System. International Journal of Computer Applications. 184, 12 ( May 2022), 44-48. DOI=10.5120/ijca2022922104

@article{ 10.5120/ijca2022922104,
author = { Alok Pratap Singh, Ankur Goel, Aakansha Goel, Diksha Arya },
title = { NLP based Grievance Redressal System },
journal = { International Journal of Computer Applications },
issue_date = { May 2022 },
volume = { 184 },
number = { 12 },
month = { May },
year = { 2022 },
issn = { 0975-8887 },
pages = { 44-48 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume184/number12/32379-2022922104/ },
doi = { 10.5120/ijca2022922104 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:21:18.555431+05:30
%A Alok Pratap Singh
%A Ankur Goel
%A Aakansha Goel
%A Diksha Arya
%T NLP based Grievance Redressal System
%J International Journal of Computer Applications
%@ 0975-8887
%V 184
%N 12
%P 44-48
%D 2022
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Internet is almost accessed by every individual and for expressing themselves and their thinking about the Politics, Country, Sports, and various other topics. Analyzing these trends of the public, can yield various result for variety of purposes. Social Media platforms are also used by several government ministries, mostly Twitter, as its main purpose is data sharing and complaint accumulation. By this, one can collect various data, sentiments, knowledge, and requirements of citizens by applying analyzing citizen sourcing ideas to provide better public service. It is hard to search for the complaint tweets, as these tweets have high velocity and are unstructured in nature. The study provides a framework that helps the Railway Ministry to classify the tweets into complaints/suggestions and compliments. The research shows the usage of Natural Language Processing (NLP) and sentiment analysis for the classification of tweets as the data set is written as general spoken language. The accuracy of the framework is 95.8%.

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Index Terms

Computer Science
Information Sciences

Keywords

Natural Language Processing Sentiment Analysis Twitter Analysis Naïve Bayes Decision Tree Random Forest Correlation and Regression