New Classification Models for Detecting Hate and Violence Web Content

Shuhua Liu, Thomas Forss

Abstract

Today, the presence of harmful and inappropriate content on the web still remains one of the most primary concerns for web users. Web classification models in the early days are limited by the methods and data available. In our research we revisit the web classification problem with the application of new methods and techniques for text content analysis. Our recent studies have indicated the promising potential of combing topic analysis and sentiment analysis in web content classification. In this paper we further explore new ways and methods to improve and maximize classification performance, especially to enhance precision and reduce false positives, thorough examination and handling of the issues with class imbalance, and through incorporation of LDA topic models.

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Paper Citation


in Harvard Style

Liu S. and Forss T. (2015). New Classification Models for Detecting Hate and Violence Web Content . In Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2015) ISBN 978-989-758-158-8, pages 487-495. DOI: 10.5220/0005636704870495


in Bibtex Style

@conference{kdir15,
author={Shuhua Liu and Thomas Forss},
title={New Classification Models for Detecting Hate and Violence Web Content},
booktitle={Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2015)},
year={2015},
pages={487-495},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005636704870495},
isbn={978-989-758-158-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2015)
TI - New Classification Models for Detecting Hate and Violence Web Content
SN - 978-989-758-158-8
AU - Liu S.
AU - Forss T.
PY - 2015
SP - 487
EP - 495
DO - 10.5220/0005636704870495