Sentiment analysis for effective detection of cyber bullying

Authors:
Vinita Nahar;Sayan Unankard;Xue Li;Chaoyi Pang
Affiliations:
School of Information Technology and Electrical Engineering, The University of Queensland, Australia;School of Information Technology and Electrical Engineering, The University of Queensland, Australia;School of Information Technology and Electrical Engineering, The University of Queensland, Australia;CSIRO, The Australian E-Health Research Center, Australia
Venue:
APWeb'12 Proceedings of the 14th Asia-Pacific international conference on Web Technologies and Applications
Year:
2012

Citing 4
Cited 0

Unsupervised learning by probabilistic latent semantic analysis

Machine Learning
A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts

ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
ARSA: a sentiment-aware model for predicting sales performance using blogs

SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Learning to Identify Internet Sexual Predation

International Journal of Electronic Commerce

Quantified Score

Hi-index	0.00

Visualization

Abstract

The rapid growth of social networking and gaming sites is associated with an increase of online bullying activities which, in the worst scenario, result in suicidal attempts by the victims. In this paper, we propose an effective technique to detect and rank the most influential persons (predators and victims). It simplifies the network communication problem through a proposed detection graph model. The experimental results indicate that this technique is highly accurate.