An application of customizable content-based filtering for unwanted messages on OSN walls
Journal Title: International Journal of Research in Computer and Communication Technology - Year 2014, Vol 3, Issue 10
Abstract
The attempt of the present work is consequently to propose and experimentally estimate an automated system called Filtered Wall (FW) which is capable to filter unwanted messages from OSN user walls. We develop Machine Learning (ML) text categorization techniques to automatically allocate with each short text message a set of categories based on its content. One essential issue in today’s Online Social Networks (OSNs) is to give users the facility to control the messages posted on their own private space to avoid that unwanted content is displayed. Up to now OSNs afford little support to this requirement. To fill the gap we propose a system allowing OSN users to have a direct organize on the messages posted on their walls. This is achieved through a flexible rule-based system that let users to adapt the filtering criterion to be applied to their walls and a Machine Learning-based soft classifier automatically labelling messages in support of content-based filtering. The main efforts in building a healthy short text classifier (STC) are concentrated in the taking out and selection of a set of characterizing and distinguish features. Another new technique machine learning text categorization techniques to automatically assign with each short text message a set of categories based on its content.
Authors and Affiliations
Jagannadha Rao Neelam, U. Chandra Sekhar Reddy
Keywords
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