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Smart and Incremental Model to Build Clustered Trending Topics of Web Documents

  • Mona A. Abou-OfEmail author
  • Hassan M. SaadEmail author
  • Saad M. DarwishEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 921)

Abstract

The abstract Social media trends, which have become more popular nowadays, introduce a rich hub of a broad spectrum of topics. It is of great importance to track emerging related topics when major events occur. The source of such information would be available not only through social portals but also through news, articles and web portals. All this information is aggregated together, by the proposed news aggregator model, to be useful for retrieving the recent popular trends of a certain category or country. The proposed model addresses the identification of semantically related topics from user preferences and favorites that are added manually by the user. Their textual contexts are acquired from the news search and then a clustering technique is applied followed by tracking of trending topics in term space. By quantitative experiments on manually annotated trends, we compared the model with two other well-known algorithms, using three different online datasets. The presented results demonstrate that the model reliably achieves a better entropy and F-measure, and so outperforms the two other mentioned algorithms.

Keywords

News aggregator WordNet Detecting and tracking topics Semantic similarity Text mining Summarization NLP Web mining Incremental clustering K-Means 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Pharos University in AlexandriaAlexandriaEgypt
  2. 2.Institute of Graduate Studies and ResearchesAlexandria UniversityAlexandriaEgypt

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