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A novel framework for sentiment classification employing Bi-GRU optimized by enhanced human evolutionary optimization

Xi Wang1, Samad Nourmohammadi2,3

  • 1Yunnan Agricultural University, Puer, Yunnan, 665099, PR China.

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|May 16, 2025
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Summary
This summary is machine-generated.

This study enhances movie review sentiment analysis using Bidirectional Gated Recurrent Unit (Bi-GRU) optimized with Enhanced Human Evolutionary Optimization (EHEO). The Bi-GRU/EHEO model with Word2Vec achieved superior accuracy, demonstrating its effectiveness for real-world applications.

Keywords:
Bidirectional gated recurrent unit (Bi-GRU)Enhanced human evolutionary optimization (EHEO)GloVeSentiment analysisWord embeddingWord2 Vec

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Area of Science:

  • Natural Language Processing
  • Machine Learning
  • Computational Linguistics

Background:

  • Sentiment analysis is crucial for understanding public opinion, especially in movie reviews.
  • Movie review data presents unique challenges like text length, misspellings, and abbreviations.
  • Traditional methods require specialized approaches for effective sentiment analysis.

Purpose of the Study:

  • To develop and evaluate advanced sentiment analysis models for movie reviews.
  • To compare the effectiveness of GloVe and Word2Vec word embedding models.
  • To optimize a Bidirectional Gated Recurrent Unit (Bi-GRU) model using Enhanced Human Evolutionary Optimization (EHEO).

Main Methods:

  • Utilized GloVe and Word2Vec for word vectorization.
  • Employed a Bidirectional Gated Recurrent Unit (Bi-GRU) architecture for sentiment classification.
  • Optimized model hyperparameters using the Enhanced Human Evolutionary Optimization (EHEO) algorithm.

Main Results:

  • The Bi-GRU/EHEO model with Word2Vec achieved 98.54% precision, 97.75% recall, 97.54% accuracy, and 97.63% F1-score.
  • The Bi-GRU/EHEO model with GloVe achieved 97.26% precision, 96.37% recall, 97.42% accuracy, and 96.30% F1-score.
  • Outperformed baseline GRU and Bi-GRU models significantly.

Conclusions:

  • The proposed sentiment analysis approaches, particularly Bi-GRU/EHEO with Word2Vec, show high efficiency and accuracy for movie review analysis.
  • These models offer practical solutions for diverse industries, including customer feedback, political opinion, and social media analysis.
  • The enhanced models can aid in trend forecasting, decision-making, and textual data examination.