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Multilingual sentiment analysis in restaurant reviews using aspect focused learning.

Arifur Rahman1, Md Azam Khan1, Kanchon Kumar Bishnu2

  • 1School of Business, International American University, Los Angeles, CA, USA.

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Summary
This summary is machine-generated.

This study introduces XLM-RSA, a novel model for cross-cultural sentiment analysis in restaurant reviews. It achieves state-of-the-art results by adapting to linguistic and cultural nuances in multilingual feedback.

Keywords:
Aspect-focused attentionCross-cultural sentiment analysisDeep learningNatural language processingRestaurant reviewsSentiment classificationXLM-RoBERTa

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Area of Science:

  • Natural Language Processing
  • Computational Linguistics
  • Artificial Intelligence

Background:

  • Cross-cultural sentiment analysis faces challenges due to linguistic and cultural variations.
  • Accurate sentiment detection in multilingual restaurant reviews is crucial for global businesses.
  • Existing models often struggle with the nuances of diverse cultural expressions in feedback.

Purpose of the Study:

  • To develop a culturally adaptive sentiment analysis model for multilingual restaurant reviews.
  • To enhance sentiment detection accuracy across diverse cultural contexts.
  • To improve understanding of customer feedback in a globalized market.

Main Methods:

  • Proposed XLM-RSA, a novel multilingual model based on XLM-RoBERTa with Aspect-Focused Attention.
  • Evaluated XLM-RSA on three benchmark datasets: 10,000 Restaurant Reviews, Restaurant Reviews, and European Restaurant Reviews.
  • Introduced an aspect-based attention mechanism to capture sentiment variations for key aspects like food, service, and ambiance.

Main Results:

  • XLM-RSA achieved state-of-the-art performance across all evaluated datasets.
  • Attained 91.9% accuracy on the Restaurant Reviews dataset, outperforming BERT (87.8%) and RoBERTa (88.5%).
  • Demonstrated strong performance in detecting cultural sentiment shifts (85.4% accuracy on European Restaurant Reviews) and aspect-level improvements (91.5% F1-score with Aspect-Focused Attention).

Conclusions:

  • XLM-RSA offers effective cross-cultural sentiment analysis capabilities for multilingual restaurant reviews.
  • The Aspect-Focused Attention mechanism significantly enhances sentiment analysis accuracy.
  • The model paves the way for more accurate, sentiment-driven insights from global customer feedback.