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.
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https://archive.ics.uci.edu/ml/datasets/reuters-21578+text+categorization+collection (2007)
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Abou-Of, M.A., Saad, H.M., Darwish, S.M. (2020). Smart and Incremental Model to Build Clustered Trending Topics of Web Documents. In: Hassanien, A., Azar, A., Gaber, T., Bhatnagar, R., F. Tolba, M. (eds) The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2019). AMLTA 2019. Advances in Intelligent Systems and Computing, vol 921. Springer, Cham. https://doi.org/10.1007/978-3-030-14118-9_87
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