Classification of extremist text on the web using sentiment analysis approach

Owoeye, Kolade Olawande and Weir, George R. S. (2018) Classification of extremist text on the web using sentiment analysis approach. In: IEEE 5th International Conference on Computational Science and Computational Intelligence, 2018-12-13 - 2018-12-15.

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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.

  • Item type:Conference or Workshop Item(Paper)
    ID code:67258
    Dates:
    Date
    Event
    13 December 2018
    Published
    4 December 2018
    Accepted
    Notes:© 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.
    Subjects:Science > Mathematics > Electronic computers. Computer science
    Department:Faculty of Science > Computer and Information Sciences
    Depositing user: Pure Administrator
    Date deposited:12 Mar 2019 10:30
    Last modified:09 Apr 2024 06:37
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/67258
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