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Sentiment Analysis of Political Post Classification Based on XGBoost

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Proceedings of International Conference on Computing and Communication Networks

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 394))

Abstract

The number of Websites and the volume of posts published on the Internet have increased dramatically in recent years. The ability to automatically assess the political polarity of a post (text) can be useful in a variety of fields including security and academics. The sentiment classification of postings, on the other hand, appears to be more complicated compared to classifying the sentiment of traditional texts. The classification procedure adopted in this research uses XGBoost algorithm and bag of word as feature extraction. To test the accuracy of the approach, the study used the confusion matrix. The proposed approach achieved 95.161 accuracy percentage.

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Author information

Authors and Affiliations

  1. Informatics Institute for Postgraduate Student, Baghdad, Iraq

    Ahmed Assim Nsaif & Dhafar Hamed Abd

  2. Department of Computer Science, Al-Maarif University College, Anbar, Iraq

    Dhafar Hamed Abd

Authors
  1. Ahmed Assim Nsaif
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  2. Dhafar Hamed Abd
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Corresponding author

Correspondence to Dhafar Hamed Abd .

Editor information

Editors and Affiliations

  1. Manchester Metropolitan University, Manchester, UK

    Ali Kashif Bashir

  2. University of Calabria, Rende, Italy

    Giancarlo Fortino

  3. Maharaja Agrasen Institute of Technology, New Delhi, Delhi, India

    Ashish Khanna

  4. Maharaja Agrasen Institute of Technology, New Delhi, Delhi, India

    Deepak Gupta

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© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Nsaif, A.A., Abd, D.H. (2022). Sentiment Analysis of Political Post Classification Based on XGBoost. In: Bashir, A.K., Fortino, G., Khanna, A., Gupta, D. (eds) Proceedings of International Conference on Computing and Communication Networks. Lecture Notes in Networks and Systems, vol 394. Springer, Singapore. https://doi.org/10.1007/978-981-19-0604-6_16

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  • DOI: https://doi.org/10.1007/978-981-19-0604-6_16

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-0603-9

  • Online ISBN: 978-981-19-0604-6

  • eBook Packages: EngineeringEngineering (R0)

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