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An interpretable multi-transformer ensemble for text-based movie genre classification.

Faheem Shaukat1, Naveed Ejaz2, Zeeshan Ashraf3

  • 1Department of Computing and Technology, IQRA University, Islamabad, Pakistan.

Peerj. Computer Science
|September 24, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an ensemble deep learning model using movie plots for multi-label genre classification. The model achieves state-of-the-art results, outperforming existing methods by leveraging textual data and interpretability techniques.

Keywords:
Movie genreTextual dataTransformer

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Area of Science:

  • Artificial Intelligence
  • Natural Language Processing
  • Computer Vision

Background:

  • Multi-label movie genre classification is complex due to genre overlap.
  • Existing methods primarily rely on audio-visual data, underutilizing text.
  • Text-based approaches offer untapped potential for accurate genre prediction.

Purpose of the Study:

  • To develop an ensemble deep learning model for multi-label movie genre classification using movie plots.
  • To explore the efficacy of text-based modalities in genre prediction.
  • To enhance model interpretability using Local Interpretable Model-Agnostic Explanations (LIME).

Main Methods:

  • Pre-processing of textual movie plots.
  • Utilizing three transformer-based models: BERT, DistilBERT, and RoBERTa.
  • Combining predictions via a weighted soft-voting ensemble method.
  • Applying LIME for model interpretability.

Main Results:

  • Achieved state-of-the-art performance on Trailers12K and LMTD9 datasets.
  • Reached micro-average precision of 80.10% and 80.37%, respectively.
  • Significantly outperformed traditional and advanced deep learning models.
  • Demonstrated the effectiveness of combining diverse transformer models.

Conclusions:

  • Textual data, specifically movie plots, is highly effective for automated multi-label genre classification.
  • Ensemble deep learning models capture nuanced genre information from text.
  • Interpretability methods like LIME are crucial for understanding genre classification models.