Analysis and Classification of Crime Tweets

Lal, Sangeeta; Tiwari, Lipika; Ranjan, Ravi; Verma, Ayushi; Sardana, Neetu; Mourya, Rahul

Abstract

Nowadays social Networking and micro-blogging sites like Twitter are very popular and millions of users are registered on these websites. The users present on these website use these websites as a platform to express their thoughts and opinions. Our analysis of content posted on Twitter shows that users often post crime related information on Twitter. Among these crime related tweets some tweets are the crime messages that need police attention. Detection of such tweets can be beneficial in utilizing pattroling resources. The analysis of the data present on these websites can have an enormous impact. In this paper,the work is done on analyzing Twitter data to identify crime tweet that need police attention. Text mining based approach is used for classification of 369 tweets into crime and not-crime class. Classifiers such as Naive Bayesian, Random Forest, J48 and ZeroR are used. Among all of these four classifiers, Random forest classifier give the best accuracy of 98.1%.

Citation

Lal, S., Tiwari, L., Ranjan, R., Verma, A., Sardana, N., & Mourya, R. (2020). Analysis and Classification of Crime Tweets. Procedia Computer Science, 167, 1911-1919. https://doi.org/10.1016/j.procs.2020.03.211

• Journal Article Type: Article
• Online Publication Date: Apr 16, 2020
• Publication Date: 2020
• Deposit Date: Jul 25, 2024
• Journal: Procedia Computer Science
• Print ISSN: 1877-0509
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 167
• Pages: 1911-1919
• DOI: https://doi.org/10.1016/j.procs.2020.03.211
• Public URL: https://keele-repository.worktribe.com/output/879835
• Additional Information: This article is maintained by: Elsevier; Article Title: Analysis and Classification of Crime Tweets; Journal Title: Procedia Computer Science; CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.procs.2020.03.211; Content Type: article; Copyright: © 2020 The Author(s). Published by Elsevier B.V.