Classification of terrorism based on tweet text post on twitter using term weighting schemes

Social Network Service (SNS) has become the main platform to distribute information, sharing of experience and knowledge. The Twitter platform gained the popularity very quickly since it’s founded for all layers of generation. The popularity of Twitter has led to prominent media coverage with instan...

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Main Author:	Muhammad, Muhammad Fikri Arif
Format:	Thesis
Language:	English
Published:	2018
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/81564/1/MuhammadFikriArifMFK2018.pdf
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spelling	my-utm-ep.815642019-09-10T01:40:57Z Classification of terrorism based on tweet text post on twitter using term weighting schemes 2018 Muhammad, Muhammad Fikri Arif QA75 Electronic computers. Computer science Social Network Service (SNS) has become the main platform to distribute information, sharing of experience and knowledge. The Twitter platform gained the popularity very quickly since it’s founded for all layers of generation. The popularity of Twitter has led to prominent media coverage with instant news and advertisement from all over the world. However, the content of tweet posted on Twitter platform are not necessarily true and can sometimes be considered as a threat to another users. Workforce expertise that involve in intelligence gathering always deals with difficulty as the complexity of crime increases, human errors and time constraints. Thus, it is difficult to prevent undesired posts, such as terrorism posts, which are intended to disseminate their propaganda. Hence, an investigating for three term weighting schemes on two datasets are used to improve the automated content-based classification techniques. The research study aims to improve the content-based classification accuracy on Twitter by comparing Term Weighting Schemes in classifying terrorism contents. In this project, three different techniques for term weighting schemes namely Entropy, Term Frequency Inverse Document Frequency (TF-IDF) and Term Frequency Relevance Frequency (TFRF) are used as feature selection process in filtering Twitter posts. The performance of these techniques were examined via datasets, and the accuracy of their result was measured by Support Vector Machine (SVM). Entropy, TF-IDF and TFRF are judged based on accuracy, precision, recall and F score measurement. Results showed that TFRF performed better than Entropy and TF-IDF. It is hoped that this study would give other researchers an insight especially who want to work with similar area. 2018 Thesis http://eprints.utm.my/id/eprint/81564/ http://eprints.utm.my/id/eprint/81564/1/MuhammadFikriArifMFK2018.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:122295 masters Universiti Teknologi Malaysia Computing
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Muhammad, Muhammad Fikri Arif Classification of terrorism based on tweet text post on twitter using term weighting schemes
description	Social Network Service (SNS) has become the main platform to distribute information, sharing of experience and knowledge. The Twitter platform gained the popularity very quickly since it’s founded for all layers of generation. The popularity of Twitter has led to prominent media coverage with instant news and advertisement from all over the world. However, the content of tweet posted on Twitter platform are not necessarily true and can sometimes be considered as a threat to another users. Workforce expertise that involve in intelligence gathering always deals with difficulty as the complexity of crime increases, human errors and time constraints. Thus, it is difficult to prevent undesired posts, such as terrorism posts, which are intended to disseminate their propaganda. Hence, an investigating for three term weighting schemes on two datasets are used to improve the automated content-based classification techniques. The research study aims to improve the content-based classification accuracy on Twitter by comparing Term Weighting Schemes in classifying terrorism contents. In this project, three different techniques for term weighting schemes namely Entropy, Term Frequency Inverse Document Frequency (TF-IDF) and Term Frequency Relevance Frequency (TFRF) are used as feature selection process in filtering Twitter posts. The performance of these techniques were examined via datasets, and the accuracy of their result was measured by Support Vector Machine (SVM). Entropy, TF-IDF and TFRF are judged based on accuracy, precision, recall and F score measurement. Results showed that TFRF performed better than Entropy and TF-IDF. It is hoped that this study would give other researchers an insight especially who want to work with similar area.
format	Thesis
qualification_level	Master's degree
author	Muhammad, Muhammad Fikri Arif
author_facet	Muhammad, Muhammad Fikri Arif
author_sort	Muhammad, Muhammad Fikri Arif
title	Classification of terrorism based on tweet text post on twitter using term weighting schemes
title_short	Classification of terrorism based on tweet text post on twitter using term weighting schemes
title_full	Classification of terrorism based on tweet text post on twitter using term weighting schemes
title_fullStr	Classification of terrorism based on tweet text post on twitter using term weighting schemes
title_full_unstemmed	Classification of terrorism based on tweet text post on twitter using term weighting schemes
title_sort	classification of terrorism based on tweet text post on twitter using term weighting schemes
granting_institution	Universiti Teknologi Malaysia
granting_department	Computing
publishDate	2018
url	http://eprints.utm.my/id/eprint/81564/1/MuhammadFikriArifMFK2018.pdf
_version_	1747818359319166976

Classification of terrorism based on tweet text post on twitter using term weighting schemes

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