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International Conference on Context-Aware Systems and Applications

ICCASA 2021: Context-Aware Systems and Applications pp 56–70Cite as

Ensemble Learning for Mining Opinions on Food Reviews

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Abstract

This paper proposes an ensemble learning model for opinion mining on food reviews. The proposed model is built on an ensemble of decision trees called Random classification forest. This model performs the task of classifying sentiment about food as positive, negative, or neutral. The ensemble learning model was evaluated on two scenarios, which we built based on important features of the reviews. The experimental results on the food reviews data set have shown the effectiveness of the proposed model.

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Notes

  1. 1.

    https://cran.r-project.org/web/packages/tree/.

  2. 2.

    https://cran.r-project.org/web/packages/randomForest/.

  3. 3.

    https://cran.r-project.org/web/packages/NLP/index.html.

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Author information

Authors and Affiliations

  1. Faculty of Foreign Language and Informatics, People’s Police College II, Ho Chi Minh City, Vietnam

    Phuc Quang Tran

  2. College of Information and Communication Technology, Can Tho University, Can Tho City, Vietnam

    Hai Thanh Nguyen & Hiep Xuan Huynh

  3. Faculty of Information Technology, University of Science and Technology Da Nang University, Da Nang City, Vietnam

    Hanh My Thi Le

Authors
  1. Phuc Quang Tran
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  2. Hai Thanh Nguyen
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  3. Hanh My Thi Le
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  4. Hiep Xuan Huynh
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Corresponding author

Correspondence to Hiep Xuan Huynh .

Editor information

Editors and Affiliations

  1. Nguyen Tat Thanh University, Ho Chi Minh City, Vietnam

    Phan Cong Vinh

  2. University of the West of England, Bristol, UK

    Abdur Rakib

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© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Tran, P.Q., Nguyen, H.T., Le, H.M.T., Huynh, H.X. (2021). Ensemble Learning for Mining Opinions on Food Reviews. In: Cong Vinh, P., Rakib, A. (eds) Context-Aware Systems and Applications. ICCASA 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 409. Springer, Cham. https://doi.org/10.1007/978-3-030-93179-7_5

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  • DOI: https://doi.org/10.1007/978-3-030-93179-7_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-93178-0

  • Online ISBN: 978-3-030-93179-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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