Classification of extremist text on the web using sentiment analysis approach

Research output: Contribution to conferencePaper

Abstract

The high volume of extremist materials online makes manual classification impractical. However, there is a need for automated classification techniques. One set of extremist web pages obtained by the TENE Web-crawler was initially subjected to manual classification. A sentiment-based classification model was then developed to automate the classification of such extremist Websites. The classification model measures how well the pages could be automatically matched against their appropriate classes. The method also identifies particular data items that differ in manual classification from their automated classification. The results from our method showed that overall web pages were correctly matched against the manual classification with a 93% success rate. In addition, a feature selection algorithm was able to reduce the original 26-feature set by one feature to attain a better overall performance of 94% in classifying the Web data.

Conference

ConferenceIEEE 5th International Conference on Computational Science and Computational Intelligence
Abbreviated titleCSCI2018
CountryUnited States
CityLas Vegas
Period13/12/1815/12/18
Internet address

Fingerprint

Websites
Feature extraction
Web crawler

Keywords

  • extremism
  • sentistrength
  • classification
  • sentiment

Cite this

Owoeye, K. O., & Weir, G. R. S. (2018). Classification of extremist text on the web using sentiment analysis approach. Paper presented at IEEE 5th International Conference on Computational Science and Computational Intelligence, Las Vegas, United States.
Owoeye, Kolade Olawande ; Weir, George R. S. / Classification of extremist text on the web using sentiment analysis approach. Paper presented at IEEE 5th International Conference on Computational Science and Computational Intelligence, Las Vegas, United States.6 p.
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title = "Classification of extremist text on the web using sentiment analysis approach",
abstract = "The high volume of extremist materials online makes manual classification impractical. However, there is a need for automated classification techniques. One set of extremist web pages obtained by the TENE Web-crawler was initially subjected to manual classification. A sentiment-based classification model was then developed to automate the classification of such extremist Websites. The classification model measures how well the pages could be automatically matched against their appropriate classes. The method also identifies particular data items that differ in manual classification from their automated classification. The results from our method showed that overall web pages were correctly matched against the manual classification with a 93{\%} success rate. In addition, a feature selection algorithm was able to reduce the original 26-feature set by one feature to attain a better overall performance of 94{\%} in classifying the Web data.",
keywords = "extremism, sentistrength, classification, sentiment",
author = "Owoeye, {Kolade Olawande} and Weir, {George R. S.}",
note = "{\circledC} 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.; IEEE 5th International Conference on Computational Science and Computational Intelligence, CSCI2018 ; Conference date: 13-12-2018 Through 15-12-2018",
year = "2018",
month = "12",
day = "13",
language = "English",
url = "https://americancse.org/events/csci2018/Symposiums",

}

Owoeye, KO & Weir, GRS 2018, 'Classification of extremist text on the web using sentiment analysis approach' Paper presented at IEEE 5th International Conference on Computational Science and Computational Intelligence, Las Vegas, United States, 13/12/18 - 15/12/18, .

Classification of extremist text on the web using sentiment analysis approach. / Owoeye, Kolade Olawande; Weir, George R. S.

2018. Paper presented at IEEE 5th International Conference on Computational Science and Computational Intelligence, Las Vegas, United States.

Research output: Contribution to conferencePaper

TY - CONF

T1 - Classification of extremist text on the web using sentiment analysis approach

AU - Owoeye, Kolade Olawande

AU - Weir, George R. S.

N1 - © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

PY - 2018/12/13

Y1 - 2018/12/13

N2 - The high volume of extremist materials online makes manual classification impractical. However, there is a need for automated classification techniques. One set of extremist web pages obtained by the TENE Web-crawler was initially subjected to manual classification. A sentiment-based classification model was then developed to automate the classification of such extremist Websites. The classification model measures how well the pages could be automatically matched against their appropriate classes. The method also identifies particular data items that differ in manual classification from their automated classification. The results from our method showed that overall web pages were correctly matched against the manual classification with a 93% success rate. In addition, a feature selection algorithm was able to reduce the original 26-feature set by one feature to attain a better overall performance of 94% in classifying the Web data.

AB - The high volume of extremist materials online makes manual classification impractical. However, there is a need for automated classification techniques. One set of extremist web pages obtained by the TENE Web-crawler was initially subjected to manual classification. A sentiment-based classification model was then developed to automate the classification of such extremist Websites. The classification model measures how well the pages could be automatically matched against their appropriate classes. The method also identifies particular data items that differ in manual classification from their automated classification. The results from our method showed that overall web pages were correctly matched against the manual classification with a 93% success rate. In addition, a feature selection algorithm was able to reduce the original 26-feature set by one feature to attain a better overall performance of 94% in classifying the Web data.

KW - extremism

KW - sentistrength

KW - classification

KW - sentiment

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M3 - Paper

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Owoeye KO, Weir GRS. Classification of extremist text on the web using sentiment analysis approach. 2018. Paper presented at IEEE 5th International Conference on Computational Science and Computational Intelligence, Las Vegas, United States.