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Analyzing Arabic Twitter-Based Patient Experience Sentiments Using Multi-Dialect Arabic Bidirectional Encoder Representations from Transformers

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Computers, Materials & Continua 2023年第7期76卷 195-220页

作者： Sarab AlMuhaideb yasmeen alnegheimish Taif AlOmar Reem AlSabti Maha AlKathery Ghala AlOlyyan Department of Computer Science College of Computer and Information SciencesKing Saud UniversityP.O.Box 266Riyadh 11362Saudi Arabia

Healthcare organizations rely on patients’feedback and experiences to evaluate their performance and services,thereby allowing such organizations to improve inadequate services and address any *** to the literature,social networks and particularly Twitter are effective platforms for gathering public ***,recent studies have used natural language processing to measure sentiments in text segments collected from Twitter to capture public opinions about various sectors,including *** present study aimed to analyze Arabic Twitter-based patient experience sentiments and to introduce an Arabic patient experience *** authors collected 12,400 tweets from Arabic patients discussing patient experiences related to healthcare organizations in Saudi Arabia from 1 January 2008 to 29 January *** tweets were labeled according to sentiment(positive or negative)and sector(public or private),and thereby the Hospital Patient Experiences in Saudi Arabia(HoPE-SA)dataset was produced.A simple statistical analysis was conducted to examine differences in patient views of healthcare *** authors trained five models to distinguish sentiments in tweets automatically with the following schemes:a transformer-based model fine-tuned with deep learning architecture and a transformer-based model fine-tuned with simple architecture,using two different transformer-based embeddings based on Bidirectional Encoder Representations from Transformers(BERT),Multi-dialect Arabic BERT(MAR-BERT),and multilingual BERT(mBERT),as well as a pretrained word2vec model with a support vector machine *** is the first study to investigate the use of a bidirectional long short-term memory layer followed by a feedforward neural network for the fine-tuning of *** deep-learning fine-tuned MARBERT-based model—the authors’best-performing model—achieved accuracy,micro-F1,and macro-F1 scores of 98.71%,98.73%,and 98.63%,respectively.

关键词： Sentiment analysis patient experience healthcare Twitter MARBERT bidirectional long short-term memory support vector machine transformer-based learning deep learning

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