Crime-Intent Sentiment Detection on Twitter Data Using Machine Learning

Biodoumoye George Bokolo; Ebikela Ogegbene-Ise; Lei Chen; Qingzhong Liu

doi:10.1109/CACRE58689.2023.10208384

Crime-Intent Sentiment Detection on Twitter Data Using Machine Learning

Biodoumoye George Bokolo, Ebikela Ogegbene-Ise, Lei Chen, Qingzhong Liu

Information Technology

Research output: Contribution to book or proceeding › Conference article › peer-review

2 Scopus citations

Abstract

This research examines sentiment analysis in the context of crime intent using machine learning algorithms. A comparison is made between a crime intent dataset generated from a Twitter developer account and Kaggle's sentiment140 dataset for Twitter sentiment analysis. The algorithms employed include Support Vector Machine (SVM), Naïve Bayes, and Long Short-Term Memory (LSTM). The findings indicate that LSTM outperforms the other algorithms, achieving high accuracy (97%) and precision (99%) in detecting crime tweets. Thus, it is concluded that the crime tweets were accurately identified.

Original language	English
Title of host publication	Proceedings - 2023 8th International Conference on Automation, Control and Robotics Engineering, CACRE 2023
Editors	Fumin Zhang, Lichuan Zhang
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	79-83
Number of pages	5
ISBN (Electronic)	9798350302776
DOIs	https://doi.org/10.1109/CACRE58689.2023.10208384
State	Published - 2023
Event	8th International Conference on Automation, Control and Robotics Engineering, CACRE 2023 - Hong Kong, China Duration: Jul 13 2023 → Jul 15 2023

Publication series

Name	Proceedings - 2023 8th International Conference on Automation, Control and Robotics Engineering, CACRE 2023

Conference

Conference	8th International Conference on Automation, Control and Robotics Engineering, CACRE 2023
Country/Territory	China
City	Hong Kong
Period	07/13/23 → 07/15/23

Keywords

crime-intent
criminal
cyberbullying
LSTM
Naïve Bayes
sentiment analysis
SVM

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/CACRE58689.2023.10208384

Cite this

Bokolo, B. G., Ogegbene-Ise, E., Chen, L., & Liu, Q. (2023). Crime-Intent Sentiment Detection on Twitter Data Using Machine Learning. In F. Zhang, & L. Zhang (Eds.), Proceedings - 2023 8th International Conference on Automation, Control and Robotics Engineering, CACRE 2023 (pp. 79-83). (Proceedings - 2023 8th International Conference on Automation, Control and Robotics Engineering, CACRE 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CACRE58689.2023.10208384

Bokolo, Biodoumoye George ; Ogegbene-Ise, Ebikela ; Chen, Lei et al. / Crime-Intent Sentiment Detection on Twitter Data Using Machine Learning. Proceedings - 2023 8th International Conference on Automation, Control and Robotics Engineering, CACRE 2023. editor / Fumin Zhang ; Lichuan Zhang. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 79-83 (Proceedings - 2023 8th International Conference on Automation, Control and Robotics Engineering, CACRE 2023).

@inproceedings{36f4b5c78ae34e26a741378430c04993,

title = "Crime-Intent Sentiment Detection on Twitter Data Using Machine Learning",

abstract = "This research examines sentiment analysis in the context of crime intent using machine learning algorithms. A comparison is made between a crime intent dataset generated from a Twitter developer account and Kaggle's sentiment140 dataset for Twitter sentiment analysis. The algorithms employed include Support Vector Machine (SVM), Na{\"i}ve Bayes, and Long Short-Term Memory (LSTM). The findings indicate that LSTM outperforms the other algorithms, achieving high accuracy (97%) and precision (99%) in detecting crime tweets. Thus, it is concluded that the crime tweets were accurately identified.",

keywords = "crime-intent, criminal, cyberbullying, LSTM, Na{\"i}ve Bayes, sentiment analysis, SVM",

author = "Bokolo, {Biodoumoye George} and Ebikela Ogegbene-Ise and Lei Chen and Qingzhong Liu",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 8th International Conference on Automation, Control and Robotics Engineering, CACRE 2023 ; Conference date: 13-07-2023 Through 15-07-2023",

year = "2023",

doi = "10.1109/CACRE58689.2023.10208384",

language = "English",

series = "Proceedings - 2023 8th International Conference on Automation, Control and Robotics Engineering, CACRE 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "79--83",

editor = "Fumin Zhang and Lichuan Zhang",

booktitle = "Proceedings - 2023 8th International Conference on Automation, Control and Robotics Engineering, CACRE 2023",

address = "United States",

}

Bokolo, BG, Ogegbene-Ise, E, Chen, L & Liu, Q 2023, Crime-Intent Sentiment Detection on Twitter Data Using Machine Learning. in F Zhang & L Zhang (eds), Proceedings - 2023 8th International Conference on Automation, Control and Robotics Engineering, CACRE 2023. Proceedings - 2023 8th International Conference on Automation, Control and Robotics Engineering, CACRE 2023, Institute of Electrical and Electronics Engineers Inc., pp. 79-83, 8th International Conference on Automation, Control and Robotics Engineering, CACRE 2023, Hong Kong, China, 07/13/23. https://doi.org/10.1109/CACRE58689.2023.10208384

Crime-Intent Sentiment Detection on Twitter Data Using Machine Learning. / Bokolo, Biodoumoye George; Ogegbene-Ise, Ebikela; Chen, Lei et al.
Proceedings - 2023 8th International Conference on Automation, Control and Robotics Engineering, CACRE 2023. ed. / Fumin Zhang; Lichuan Zhang. Institute of Electrical and Electronics Engineers Inc., 2023. p. 79-83 (Proceedings - 2023 8th International Conference on Automation, Control and Robotics Engineering, CACRE 2023).

Research output: Contribution to book or proceeding › Conference article › peer-review

TY - GEN

T1 - Crime-Intent Sentiment Detection on Twitter Data Using Machine Learning

AU - Bokolo, Biodoumoye George

AU - Ogegbene-Ise, Ebikela

AU - Chen, Lei

AU - Liu, Qingzhong

PY - 2023

Y1 - 2023

N2 - This research examines sentiment analysis in the context of crime intent using machine learning algorithms. A comparison is made between a crime intent dataset generated from a Twitter developer account and Kaggle's sentiment140 dataset for Twitter sentiment analysis. The algorithms employed include Support Vector Machine (SVM), Naïve Bayes, and Long Short-Term Memory (LSTM). The findings indicate that LSTM outperforms the other algorithms, achieving high accuracy (97%) and precision (99%) in detecting crime tweets. Thus, it is concluded that the crime tweets were accurately identified.

AB - This research examines sentiment analysis in the context of crime intent using machine learning algorithms. A comparison is made between a crime intent dataset generated from a Twitter developer account and Kaggle's sentiment140 dataset for Twitter sentiment analysis. The algorithms employed include Support Vector Machine (SVM), Naïve Bayes, and Long Short-Term Memory (LSTM). The findings indicate that LSTM outperforms the other algorithms, achieving high accuracy (97%) and precision (99%) in detecting crime tweets. Thus, it is concluded that the crime tweets were accurately identified.

KW - crime-intent

KW - criminal

KW - cyberbullying

KW - LSTM

KW - Naïve Bayes

KW - sentiment analysis

KW - SVM

UR - http://www.scopus.com/inward/record.url?scp=85169420173&partnerID=8YFLogxK

U2 - 10.1109/CACRE58689.2023.10208384

DO - 10.1109/CACRE58689.2023.10208384

M3 - Conference article

AN - SCOPUS:85169420173

T3 - Proceedings - 2023 8th International Conference on Automation, Control and Robotics Engineering, CACRE 2023

SP - 79

EP - 83

BT - Proceedings - 2023 8th International Conference on Automation, Control and Robotics Engineering, CACRE 2023

A2 - Zhang, Fumin

A2 - Zhang, Lichuan

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 8th International Conference on Automation, Control and Robotics Engineering, CACRE 2023

Y2 - 13 July 2023 through 15 July 2023

ER -

Bokolo BG, Ogegbene-Ise E, Chen L, Liu Q. Crime-Intent Sentiment Detection on Twitter Data Using Machine Learning. In Zhang F, Zhang L, editors, Proceedings - 2023 8th International Conference on Automation, Control and Robotics Engineering, CACRE 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 79-83. (Proceedings - 2023 8th International Conference on Automation, Control and Robotics Engineering, CACRE 2023). doi: 10.1109/CACRE58689.2023.10208384

Crime-Intent Sentiment Detection on Twitter Data Using Machine Learning

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Keywords

UN SDGs

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