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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
http://eprints.utm.my/id/eprint/81564/
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spelling my.utm.815642019-09-10T01:40:57Z http://eprints.utm.my/id/eprint/81564/ Classification of terrorism based on tweet text post on twitter using term weighting schemes 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 NonPeerReviewed application/pdf en http://eprints.utm.my/id/eprint/81564/1/MuhammadFikriArifMFK2018.pdf Muhammad, Muhammad Fikri Arif (2018) Classification of terrorism based on tweet text post on twitter using term weighting schemes. Masters thesis, Universiti Teknologi Malaysia. http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:122295
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
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
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
publishDate 2018
url http://eprints.utm.my/id/eprint/81564/1/MuhammadFikriArifMFK2018.pdf
http://eprints.utm.my/id/eprint/81564/
http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:122295
_version_ 1646010297687736320
score 13.211869

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