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Fake Review Prediction Using Machine Learning

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Intelligent Systems Design and Applications (ISDA 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 717))

  • 235 Accesses

Abstract

Fake Review prediction is one of the Complex areas to be scrutinized to know the original Commands. In Recent years have seen a change in movie reviews based on the expectations and frame of mind of the audience, which is crucial to a film's success or failure. After reading the review, individuals began to watch the film, which fixed their perception of the plot. The research we conducted to create a machine learning model that can determine if user reviews on the IMBD Movie Dataset are authentic or fake is summarized in this publication. To determine which machine learning categorization approach would produce the best results, we specifically applied and contrasted them. To make it easier to understand why some approaches are preferable to others in specific situations, comprehensive explanations are provided for each of the categorization strategies. The Support Vector Machine (SVM) classifier, which had an accuracy of 89.49%, produced the best results.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, KPR Institute of Engineering and Technology Coimbatore, Coimbatore, India

    C Sasikala, Rajasekaran Thangaraj, S RajeshKumar, Ramachandramoorthy K. B, S Ramya & K Umapathi

  2. Department of Computer Science and Engineering, Centre for IOT and AI, KPR Institute of Engineering and Technology Coimbatore, Coimbatore, India

    Devipriya R

Authors
  1. C Sasikala
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  2. Rajasekaran Thangaraj
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  3. Devipriya R
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  4. S RajeshKumar
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  5. Ramachandramoorthy K. B
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  6. S Ramya
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  7. K Umapathi
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Corresponding author

Correspondence to Ramachandramoorthy K. B .

Editor information

Editors and Affiliations

  1. Faculty of Computing and Data Science, FLAME University, Pune, Maharashtra, India

    Ajith Abraham

  2. Center for Smart Computing Continuum, Burgenland, Austria

    Sabri Pllana

  3. University of Bari, Bari, Italy

    Gabriella Casalino

  4. University of Jinan, Jinan, Shandong, China

    Kun Ma

  5. Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab, India

    Anu Bajaj

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Sasikala, C. et al. (2023). Fake Review Prediction Using Machine Learning. In: Abraham, A., Pllana, S., Casalino, G., Ma, K., Bajaj, A. (eds) Intelligent Systems Design and Applications. ISDA 2022. Lecture Notes in Networks and Systems, vol 717. Springer, Cham. https://doi.org/10.1007/978-3-031-35510-3_50

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