Article Recommendation and Comics Story Representation for Twitter User Based Preferences

Authors

  • S. Vivekanandan  Department of CSE, Velalar College of Engineering and Technology, Erode, Tamil Nadu, India
  • Swathi. N  PG Scholar, Department of CSE, Velalar College of Engineering and Technology, Erode, Tamil Nadu, India

DOI:

https://doi.org//10.32628/IJSRST207226

Keywords:

Microblogging,Recommendation system,Comics story analysis,Comics genre classification,Recommendation algorithms

Abstract

Twitter is an interesting platform for the dissemination of news. The real-time nature and brevity of the tweets are conductive to sharing of information related to important events as they unfold.Numerous consumer reviews of topics are now available on the Internet. Automatically identifies the important aspects of topics from online consumer reviews. Our method provides an efficient way to accurately categorize comic topic recommendation without need of external data, enabling news organizations to discover breaking news in real-time, or to quickly identify viral memes that might enrich marketing decisions, among others. We filter the stream of incoming tweets to remove junk tweets using a text classification algorithm.We also compare the performance of different supervised SVM text classification algorithms for this task. This study concentrates on analyzing potential and dynamic user correlations, based on topic-aware similarity and behavioral influence, which may help us to discover communities in social networking sites.

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International Journal of Scientific Research in Science and Technology

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Published

2020-04-30

Issue

Volume 7, Issue 2, March-April-2020

Section

Research Articles

License

Copyright (c) IJSRST

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
S. Vivekanandan, Swathi. N, " Article Recommendation and Comics Story Representation for Twitter User Based Preferences, International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 7, Issue 2, pp.105-111, March-April-2020. Available at doi : https://doi.org/10.32628/IJSRST207226