New York City Street Cleanliness: Applying Text Mining Techniques to Social Media Information

43 Pages Posted: 20 Nov 2020 Last revised: 27 Feb 2021

See all articles by Huijue Kelly Duan

Huijue Kelly Duan

Rutgers Business School - Rutgers University

Mauricio Mello Codesso

Rutgers Business School - Rutgers University

Zamil Alzamil

Computer Science Department, College of Computer and Information Sciences, Majmaah University

Miklos A. Vasarhelyi

Rutgers Business School

Date Written: September 1, 2020

Abstract

This study examines social media information within the context of social network theory by utilizing text mining techniques and machine learning algorithms to analyze the tweets and use the information as a proxy to measure the performance of the municipal services. The objective of this study is to provide the government an alternative measure by examining user-generated content, identifying temporal trends and patterns of street cleanliness, evaluating whether people’s opinions are consistent with NYC cleanliness ratings, and assessing the performance of municipal services via sentiment analysis. The results indicate that the analytical approach presented in this study can systematically analyze social media information, and the information can be used as an alternative measure or supplementary evaluation tool to validate the government’s cleanliness ratings and evaluate the performance of the municipal services. This study extends the research on accessing social media feeds to gain valuable insights regarding municipal services’ performance. The analytical approach can be generalized to many other areas where social media information can be utilized.

Keywords: Social Media, Text Mining, Machine Learning, Sentiment Analysis

JEL Classification: M40, M48

Suggested Citation

Duan, Huijue Kelly and Mello Codesso, Mauricio and Alzamil, Zamil and Vasarhelyi, Miklos A., New York City Street Cleanliness: Applying Text Mining Techniques to Social Media Information (September 1, 2020). Available at SSRN: https://ssrn.com/abstract=3706506 or http://dx.doi.org/10.2139/ssrn.3706506

Huijue Kelly Duan (Contact Author)

Rutgers Business School - Rutgers University ( email )

Mauricio Mello Codesso

Rutgers Business School - Rutgers University

100 Rockafeller Rd, Piscataway, NJ 08854
100 Rockafeller Rd, Piscataway, NJ 08854
Piscataway, NJ New Jersey 08854
United States

Zamil Alzamil

Computer Science Department, College of Computer and Information Sciences, Majmaah University ( email )

Al-Majmaah
11952
Saudi Arabia

Miklos A. Vasarhelyi

Rutgers Business School ( email )

180 University Avenue
Ackerson Hall, Room 315
Newark, NJ 07102
United States
973-353-5002 (Phone)
973-353-1283 (Fax)

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
99
Abstract Views
525
rank
356,465
PlumX Metrics