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Improving Social Trend Detection Based on User Interaction and Combined with Keyphrase Extraction Using Text Features on Word Graph

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Part of the Lecture Notes in Networks and Systems book series (LNNS,volume 243)

Abstract

Recently, by the explosion of information technology, the valuable and available data exponentially increases in various social media platforms which allow us to exploit and attain convenient information and transform it into knowledge. This means that prominent topics are extracted on time in the social media community by leveraging the proper techniques and methods. Although there are various novel approaches in this area, they almost ignore the factors of the user interactions. Besides, since the enormous size of the textual dataset is distributed to any languages and the requirements for trending detection in a specific language, most of the proposed methods concentrating on English. In this paper, we proposed a graph-based method for the Vietnamese dataset in which graph nodes represent posts. More specifically, the approach combines the user interactions with a word graph representation which then is extracted to the topic trends by the RankClus Algorithm. By applying our proposal in several Facebook and Twitter datasets, we introduce dominantly dependable and coherent results.

Keywords

  • Social trending
  • Keyphrase extraction
  • Word graph

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Acknowledgment

This research is funded by CMC Institute of Science and Technology (CIST), CMC Corporation, Vietnam.

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Correspondence to XuanTruong Dinh .

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Dinh, X., Trinh, T., Ngoc, T., Pham, V. (2021). Improving Social Trend Detection Based on User Interaction and Combined with Keyphrase Extraction Using Text Features on Word Graph. In: Tran, DT., Jeon, G., Nguyen, T.D.L., Lu, J., Xuan, TD. (eds) Intelligent Systems and Networks . ICISN 2021. Lecture Notes in Networks and Systems, vol 243. Springer, Singapore. https://doi.org/10.1007/978-981-16-2094-2_21

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