Classifying information sender of web documents
Abstract
Purpose
To develop a method for classifying information sender of web documents, which constitutes an important part of information credibility analysis.
Design/methodology/approach
Machine learning approach was employed. About 2,000 human‐annotated web documents were prepared for training and evaluation. The classification model was based on support vector machine, and the features used for the classification included the title and URL of documents, as well as information of the top page.
Findings
With relatively small set of features, the proposed method achieved over 50 per cent accuracy.
Research limitations/implications
Some of the information sender categories were found to be more difficult to classify. This is due to the subjective nature of the categories, and further refinement of the categories is needed.
Practical implications
When combined with opinion/sentiment analysis techniques, information sender classification allows more profound analysis based on interactions between opinions and senders. Such analysis forms a basis of information credibility analysis.
Originality/value
This study formulated the problem of information sender classification. It proposed a method which achieves moderate performance. It also identified some of the issues related to information sender classification.
Keywords
Citation
Kato, Y., Kurohashi, S. and Inui, K. (2008), "Classifying information sender of web documents", Internet Research, Vol. 18 No. 2, pp. 191-203. https://doi.org/10.1108/10662240810862248
Publisher
:Emerald Group Publishing Limited
Copyright © 2008, Emerald Group Publishing Limited