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DistilRoBiLSTMFuse: an efficient hybrid deep learning approach for sentiment analysis.

Sonia Khan Papia1, Md Asif Khan2, Tanvir Habib2

  • 1Information Technology, Washington University of Science & Technology, Alexandria, VA, United States of America.

Peerj. Computer Science
|December 9, 2024
PubMed
Summary

This study introduces DistilRoBiLSTMFuse, a hybrid model for sentiment analysis (SA) that excels at understanding complex sentences and diverse language. It achieves high accuracy on benchmark datasets, improving upon existing methods for sentiment classification.

Keywords:
Deep learningDistilRoBiLSTMFuseHybrid modelIMDbMachine learningNLPSentiment analysisUSAirline twitter

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Area of Science:

  • Natural Language Processing
  • Machine Learning
  • Computational Linguistics

Background:

  • Social media generates vast amounts of text data, necessitating effective sentiment analysis (SA) for understanding public opinion.
  • Existing SA methods struggle with challenges like diverse language, data imbalance, and complex sentence structures.
  • Accurate sentiment classification is crucial for various applications, from market research to social trend analysis.

Purpose of the Study:

  • To propose and evaluate a novel hybrid architecture, DistilRoBiLSTMFuse, for enhanced sentiment analysis.
  • To address the limitations of current SA techniques in handling complex linguistic nuances and data heterogeneity.
  • To demonstrate the superior performance of the proposed model on established benchmark datasets.

Main Methods:

  • Development of the DistilRoBiLSTMFuse hybrid architecture, integrating deep contextual information extraction capabilities.
  • Implementation of a rigorous preprocessing pipeline including data cleaning, custom stopword lists, and lemmatization.
  • Application of oversampling techniques to mitigate class imbalance issues and comparative evaluation against seven ML models using TF-IDF and BoW features.

Main Results:

  • The DistilRoBiLSTMFuse model achieved state-of-the-art performance on both the IMDb and Twitter USAirline Sentiment datasets.
  • Achieved high accuracy rates: 93.97% (test) on IMDb and 98.33% (test) on Twitter USAirline Sentiment.
  • The hybrid model consistently outperformed existing approaches, validating its effectiveness in sentiment classification.

Conclusions:

  • The DistilRoBiLSTMFuse model offers a robust and effective solution for sentiment analysis, particularly for complex and noisy text data.
  • The proposed architecture successfully extracts deep contextual information, leading to superior sentiment classification accuracy.
  • The study provides a valuable contribution to the field of natural language processing and sentiment analysis, with publicly available code for reproducibility.