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A Tweet Summarization Method Based on Maximal Association Rules

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Computational Collective Intelligence (ICCCI 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11055))

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Abstract

A lot of information about different topics is posted by users on Twitter in just one second. People only want a way to get short, full, and accurate content which they are interested in receiving information. Tweet summarization to create that short text is a convenient solution to solve this problem. Many previous works were trying to solve the Tweet summarization problem. However, those researchers generated short texts based on the frequency of words in Tweet. They ignored word order in each Tweet. Moreover, they rarely considered the semantics of the words. This study tries to solve existing on above. The significant contribution of this study is to propose a new method to summary the semantics of the tweets based on mining the maximal association rules on a set of real data. The experiment results show that this proposal improves the accuracy of a summary, in comparison with other methods.

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Notes

  1. 1.

    http://douglasduhaime.com/posts/clustering-semantic-vectors.html.

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Acknowledgment

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT&Future Planning (2017R1A2B4009410).

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Yeungnam University, Gyeongsan, Republic of Korea

    Huyen Trang Phan & Dosam Hwang

  2. Faculty of Computer Science and Management, Wroclaw University of Science and Technology, Wroclaw, Poland

    Ngoc Thanh Nguyen

Authors
  1. Huyen Trang Phan
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  2. Ngoc Thanh Nguyen
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  3. Dosam Hwang
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Corresponding author

Correspondence to Dosam Hwang .

Editor information

Editors and Affiliations

  1. Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Ngoc Thanh Nguyen

  2. Department of Computer Science and Creative Technologies, University of the West of England, Bristol, United Kingdom

    Elias Pimenidis

  3. Department of Computer Science and Creative Technologies, University of the West of England, Bristol, United Kingdom

    Zaheer Khan

  4. Faculty of Computer Science and Management, Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

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Phan, H.T., Nguyen, N.T., Hwang, D. (2018). A Tweet Summarization Method Based on Maximal Association Rules. In: Nguyen, N., Pimenidis, E., Khan, Z., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2018. Lecture Notes in Computer Science(), vol 11055. Springer, Cham. https://doi.org/10.1007/978-3-319-98443-8_34

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  • DOI: https://doi.org/10.1007/978-3-319-98443-8_34

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-98442-1

  • Online ISBN: 978-3-319-98443-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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