Trending Hash Tag Using IFSS Algorithm

Authors

  • N.NagaJothi  UG Scholar, Department of CSE, Mangayarkarasi College of Engg, Paravai, Tamil Nadu, India
  • S.S.Nandhini  UG Scholar, Department of CSE, Mangayarkarasi College of Engg, Paravai, Tamil Nadu, India
  • S.Thiranya  UG Scholar, Department of CSE, Mangayarkarasi College of Engg, Paravai, Tamil Nadu, India
  • I.Muthu Meenatchi  Assistant Professor, Department of CSE, Mangayarkarasi College of Engg, Paravai, Tamil Nadu, India

Keywords:

News Aggregator, Trending Topics Detection, Semantic Similarity, Text and Web Mining, NLP, Incremental FCM Clustering.

Abstract

At this moment, a huge number of people engage in social networks is provoke a fast and wide spectrum of topics.Such trending topics are usually derived from the most frequent searches, the published posts and the daily new.The automated analysis for such data requires topics detection and tracking methods.Many challenges are being faced.web document often consists of several topics, the suggested model employs a fuzzy C-Means (FCM) clustering based trending topics detection.It applies a semantic document similarity algorithm to determine such vagueness issues caused by the usage of synonyms, homonyms or different abstraction levels.This algorithm is also used to summarize the long documents. Furthermore, an incremental clustering technique is utilized to preserve high cohesiveness up-to-date top trending topics.

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International Journal of Scientific Research in Science and Technology

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Published

2021-04-10

Issue

Volume 9, Issue 1, March-April-2021

Section

Research Articles

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How to Cite

[1]
N.NagaJothi, S.S.Nandhini, S.Thiranya, I.Muthu Meenatchi, " Trending Hash Tag Using IFSS Algorithm, International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 9, Issue 1, pp.1108-1117, March-April-2021.