Political Security Threat Prediction Framework Using Hybrid Lexicon-Based Approach and Machine Learning Technique
The internet offers a powerful medium for expressing opinions, emotions and ideas, using online platforms supported by smartphone usage and high internet penetration. Most internet posts are textual based and can include people’s emotional feelings for a particular moment or sentiment. Mo...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10048518/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850126648733597696 |
|---|---|
| author | Noor Afiza Mat Razali Nur Atiqah Malizan Nor Asiakin Hasbullah Muslihah Wook Norulzahrah Mohd Zainuddin Khairul Khalil Ishak Suzaimah Ramli Sazali Sukardi |
| author_facet | Noor Afiza Mat Razali Nur Atiqah Malizan Nor Asiakin Hasbullah Muslihah Wook Norulzahrah Mohd Zainuddin Khairul Khalil Ishak Suzaimah Ramli Sazali Sukardi |
| author_sort | Noor Afiza Mat Razali |
| collection | DOAJ |
| description | The internet offers a powerful medium for expressing opinions, emotions and ideas, using online platforms supported by smartphone usage and high internet penetration. Most internet posts are textual based and can include people’s emotional feelings for a particular moment or sentiment. Monitoring online sentiments or opinions is important for detecting any excessive emotions triggered by citizens which can lead to unintended consequences and threats to national security. Riots and civil war, for instance, must be addressed due to the risk of jeopardizing social stability and political security, which are crucial elements of national security. Mining opinions according to the national security domain is a relevant research topic that must be enhanced. Mechanisms and techniques that can mine opinions in the aspect of political security require significant improvements to obtain optimum results. Researchers have noted that there is a strong relationship between emotion, sentiment and political security threats. This study proposes a new theoretical framework for predicting political security threats using a hybrid technique: the combination of lexicon-based approach and machine learning in cyberspace. In the proposed framework, Decision Tree, Naive Bayes, and Support Vector Machine have been deployed as threat classifiers. To validate our proposed framework, an experimental analysis is accomplished. The performance of each technique used in the experiments is reported. In this study, our proposed framework reveals that the hybrid Lexicon-based approach with the Decision Tree classifier recorded the highest performance score for predicting political security threats. These findings offer valuable insight to ongoing research on opinion mining in predicting threats based on the political security domain. |
| format | Article |
| id | doaj-art-c780879b5dcb4f31888e86ceb9a3b366 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-c780879b5dcb4f31888e86ceb9a3b3662025-08-20T02:33:52ZengIEEEIEEE Access2169-35362023-01-0111171511716410.1109/ACCESS.2023.324616210048518Political Security Threat Prediction Framework Using Hybrid Lexicon-Based Approach and Machine Learning TechniqueNoor Afiza Mat Razali0https://orcid.org/0000-0001-5149-3907Nur Atiqah Malizan1Nor Asiakin Hasbullah2Muslihah Wook3https://orcid.org/0000-0002-2075-4753Norulzahrah Mohd Zainuddin4Khairul Khalil Ishak5Suzaimah Ramli6Sazali Sukardi7Faculty of Defence Science and Technology, National Defence University of Malaysia, Kuala Lumpur, MalaysiaFaculty of Defence Science and Technology, National Defence University of Malaysia, Kuala Lumpur, MalaysiaFaculty of Defence Science and Technology, National Defence University of Malaysia, Kuala Lumpur, MalaysiaFaculty of Defence Science and Technology, National Defence University of Malaysia, Kuala Lumpur, MalaysiaFaculty of Defence Science and Technology, National Defence University of Malaysia, Kuala Lumpur, MalaysiaMaster of Technology, Management and Science University, Selangor, MalaysiaFaculty of Defence Science and Technology, National Defence University of Malaysia, Kuala Lumpur, MalaysiaCyberSecurity Malaysia, Selangor, MalaysiaThe internet offers a powerful medium for expressing opinions, emotions and ideas, using online platforms supported by smartphone usage and high internet penetration. Most internet posts are textual based and can include people’s emotional feelings for a particular moment or sentiment. Monitoring online sentiments or opinions is important for detecting any excessive emotions triggered by citizens which can lead to unintended consequences and threats to national security. Riots and civil war, for instance, must be addressed due to the risk of jeopardizing social stability and political security, which are crucial elements of national security. Mining opinions according to the national security domain is a relevant research topic that must be enhanced. Mechanisms and techniques that can mine opinions in the aspect of political security require significant improvements to obtain optimum results. Researchers have noted that there is a strong relationship between emotion, sentiment and political security threats. This study proposes a new theoretical framework for predicting political security threats using a hybrid technique: the combination of lexicon-based approach and machine learning in cyberspace. In the proposed framework, Decision Tree, Naive Bayes, and Support Vector Machine have been deployed as threat classifiers. To validate our proposed framework, an experimental analysis is accomplished. The performance of each technique used in the experiments is reported. In this study, our proposed framework reveals that the hybrid Lexicon-based approach with the Decision Tree classifier recorded the highest performance score for predicting political security threats. These findings offer valuable insight to ongoing research on opinion mining in predicting threats based on the political security domain.https://ieeexplore.ieee.org/document/10048518/Cyberspacelexicon-based approachmachine learningnational securityopinion miningpolitical security |
| spellingShingle | Noor Afiza Mat Razali Nur Atiqah Malizan Nor Asiakin Hasbullah Muslihah Wook Norulzahrah Mohd Zainuddin Khairul Khalil Ishak Suzaimah Ramli Sazali Sukardi Political Security Threat Prediction Framework Using Hybrid Lexicon-Based Approach and Machine Learning Technique IEEE Access Cyberspace lexicon-based approach machine learning national security opinion mining political security |
| title | Political Security Threat Prediction Framework Using Hybrid Lexicon-Based Approach and Machine Learning Technique |
| title_full | Political Security Threat Prediction Framework Using Hybrid Lexicon-Based Approach and Machine Learning Technique |
| title_fullStr | Political Security Threat Prediction Framework Using Hybrid Lexicon-Based Approach and Machine Learning Technique |
| title_full_unstemmed | Political Security Threat Prediction Framework Using Hybrid Lexicon-Based Approach and Machine Learning Technique |
| title_short | Political Security Threat Prediction Framework Using Hybrid Lexicon-Based Approach and Machine Learning Technique |
| title_sort | political security threat prediction framework using hybrid lexicon based approach and machine learning technique |
| topic | Cyberspace lexicon-based approach machine learning national security opinion mining political security |
| url | https://ieeexplore.ieee.org/document/10048518/ |
| work_keys_str_mv | AT noorafizamatrazali politicalsecuritythreatpredictionframeworkusinghybridlexiconbasedapproachandmachinelearningtechnique AT nuratiqahmalizan politicalsecuritythreatpredictionframeworkusinghybridlexiconbasedapproachandmachinelearningtechnique AT norasiakinhasbullah politicalsecuritythreatpredictionframeworkusinghybridlexiconbasedapproachandmachinelearningtechnique AT muslihahwook politicalsecuritythreatpredictionframeworkusinghybridlexiconbasedapproachandmachinelearningtechnique AT norulzahrahmohdzainuddin politicalsecuritythreatpredictionframeworkusinghybridlexiconbasedapproachandmachinelearningtechnique AT khairulkhalilishak politicalsecuritythreatpredictionframeworkusinghybridlexiconbasedapproachandmachinelearningtechnique AT suzaimahramli politicalsecuritythreatpredictionframeworkusinghybridlexiconbasedapproachandmachinelearningtechnique AT sazalisukardi politicalsecuritythreatpredictionframeworkusinghybridlexiconbasedapproachandmachinelearningtechnique |