Web Content Classification based on Topic and Sentiment Analysis of Text

Shuhua Liu, Thomas Forss

2014

Abstract

Automatic classification of web content has been studied extensively, using different learning methods and tools, investigating different datasets to serve different purposes. Most of the studies have made use of the content and structural features of web pages. However, previous experience has shown that certain groups of web pages, such as those that contain hatred and violence, are much harder to classify with good accuracy when both content and structural features are already taken into consideration. In this study we present a new approach for automatically classifying web pages into pre-defined topic categories. We apply text summarization and sentiment analysis techniques to extract topic and sentiment indicators of web pages. We then build classifiers based on combined topic and sentiment features. A large amount of experiments were carried out. Our results suggest that incorporating the sentiment dimension can indeed bring much added value to web content classification. Topic similarity based classifiers solely did not perform well, but when topic similarity and sentiment features are combined, the classification model performance is significantly improved for many web categories. Our study offers valuable insights and inputs to the development of web detection systems and Internet safety solutions.

Paper Citation

in Harvard Style

Liu S. and Forss T. (2014). Web Content Classification based on Topic and Sentiment Analysis of Text . In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2014) ISBN 978-989-758-048-2, pages 300-307. DOI: 10.5220/0005101803000307

in Bibtex Style

@conference{kdir14,
author={Shuhua Liu and Thomas Forss},
title={Web Content Classification based on Topic and Sentiment Analysis of Text},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2014)},
year={2014},
pages={300-307},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005101803000307},
isbn={978-989-758-048-2},
}

in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2014)
TI - Web Content Classification based on Topic and Sentiment Analysis of Text
SN - 978-989-758-048-2
AU - Liu S.
AU - Forss T.
PY - 2014
SP - 300
EP - 307
DO - 10.5220/0005101803000307