Incorporating Sentiment Analysis for Improved Tag-Based Recommendation

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Qingbiao, Zhou, Jie, Fang and Xu, Guandong (2011) Incorporating Sentiment Analysis for Improved Tag-Based Recommendation. In: 2011 IEEE Ninth International Conference on Dependable, Autonomic and Secure Computing (DASC). Springer, Piscataway, N.J., pp. 1222-1227.

Abstract

Social tagging systems have become as a popular application with the advance of Web 2.0 technologies. By tagging, users annotate and index the resources freely and subjectively, based on their senses of interests, which can improve the performance of the current personalized recommendation systems. In this paper, we propose a sentiment enhanced tag-based recommendation approach by incorporating sentiment analysis of tags that annotated on resources. The presented approach introduces a sentiment enhancement factor to the similarity metric which measures the matching between resources. The evaluation results on a real datasets have demonstrated that our approach can outperform the other compared approaches in terms of recommendation precision.

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Additional Information

Conference held: Sydney, 12-14 Dec. 2011

Item type Book Section
URI https://vuir.vu.edu.au/id/eprint/9620
DOI https://doi.org/10.1109/DASC.2011.198
Official URL http://ieeexplore.ieee.org/xpl/articleDetails.jsp?...
ISBN 9780769546124
Subjects Historical > Faculty/School/Research Centre/Department > School of Engineering and Science
Historical > FOR Classification > 0806 Information Systems
Historical > SEO Classification > 8902 Computer Software and Services
Keywords ResPubID23725, social annotation systems, tag recommender systems, Internet, social networking, networks, online community, recommender systems, Web 2.0 technologies, improved tag based recommendation, sentiment enhancement factor, social tagging systems
Citations in Scopus 6 - View on Scopus
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