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SBAR is an effective communication tool used by healthcare professionals to communicate patient information accurately. SBAR stands for Situation, Background, Assessment, and Recommendation. For a better understanding, an example is given below.
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Using the AraBERT Model for Customer Satisfaction Classification of Telecom Sectors in Saudi Arabia.

Sulaiman Aftan1, Habib Shah2

  • 1Department of Computer Science, Texas Tech University, Lubbock, TX 79709, USA.

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|January 21, 2023
PubMed
Summary

This study predicts customer satisfaction using sentiment analysis on Arabic Twitter data. The Arabic Bidirectional Encoder Representations from Transformers (AraBERT) model outperformed other deep learning methods, achieving high accuracy in feedback prediction for Saudi telecom companies.

Keywords:
AraBERTBERTcustomer satisfaction classificationdeep learning

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Area of Science:

  • Natural Language Processing (NLP)
  • Artificial Intelligence (AI)
  • Machine Learning (ML)

Background:

  • Customer satisfaction and loyalty are vital for business success.
  • Predicting customer feedback and classifying social media data are crucial for understanding satisfaction levels.
  • Analyzing Arabic datasets presents unique challenges compared to English for AI models.

Purpose of the Study:

  • To present a sentiment analysis-based approach for customer feedback prediction using Arabic Twitter data.
  • To evaluate the performance of the Arabic Bidirectional Encoder Representations from Transformers (AraBERT) model against other deep learning algorithms for customer satisfaction prediction.
  • To investigate the effectiveness of AI-driven methods in analyzing telecommunications customer feedback in Saudi Arabia.

Main Methods:

  • Utilized Arabic Twitter datasets from Saudi telecommunications companies.
  • Employed the Arabic Bidirectional Encoder Representations from Transformers (AraBERT) model, analyzing various parameters like activation functions and topologies.
  • Compared AraBERT's prediction accuracy with Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) algorithms.

Main Results:

  • All evaluated deep learning methods demonstrated successful application and high classification accuracy.
  • The AraBERT model achieved superior prediction accuracy compared to CNN and RNN.
  • AraBERT showed particularly strong performance with Mobily and STC telecom datasets.

Conclusions:

  • Sentiment analysis using advanced AI models like AraBERT is effective for predicting customer satisfaction from Arabic social media data.
  • AraBERT offers a significant advantage for analyzing Arabic text data in customer feedback prediction tasks.
  • The findings highlight the potential of AI in enhancing business understanding of customer sentiment in the Saudi Arabian telecommunications sector.