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Machine Learning Techniques for Filtering of Unwanted Messages

by J. Hari Purushotham, B. Tarakeswara Rao, B. Sathyanarayana Reddy
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 140 - Number 13
Year of Publication: 2016
Authors: J. Hari Purushotham, B. Tarakeswara Rao, B. Sathyanarayana Reddy
10.5120/ijca2016909530

J. Hari Purushotham, B. Tarakeswara Rao, B. Sathyanarayana Reddy . Machine Learning Techniques for Filtering of Unwanted Messages. International Journal of Computer Applications. 140, 13 ( April 2016), 5-8. DOI=10.5120/ijca2016909530

@article{ 10.5120/ijca2016909530,
author = { J. Hari Purushotham, B. Tarakeswara Rao, B. Sathyanarayana Reddy },
title = { Machine Learning Techniques for Filtering of Unwanted Messages },
journal = { International Journal of Computer Applications },
issue_date = { April 2016 },
volume = { 140 },
number = { 13 },
month = { April },
year = { 2016 },
issn = { 0975-8887 },
pages = { 5-8 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume140/number13/24670-2016909530/ },
doi = { 10.5120/ijca2016909530 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:42:10.401282+05:30
%A J. Hari Purushotham
%A B. Tarakeswara Rao
%A B. Sathyanarayana Reddy
%T Machine Learning Techniques for Filtering of Unwanted Messages
%J International Journal of Computer Applications
%@ 0975-8887
%V 140
%N 13
%P 5-8
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Online Social Networking in these days is most powerful way to share the Information, thought, event and many more. In the Definition of technology we usually follow the Industry of Information Technology where, we describe the high-level contributions of this paper and discuss potential future research directions. Despite the massive popularity of online social networks, surprisingly little is known about how people are using them to connect and share content. To better understand the structure of online social networks, in this paper we conducted a large-scale measurement study that collected data on the social networks of four popular sites, covering over 12 million users and 400 million links, in order to remove the unwanted messages. Machine Learning Text Categorization is also used to categorize the short text messages.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Online social networks information filtering short text classification policy-based personalization.

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