Hospital Facebook Reviews Analysis Using a Machine Learning Sentiment Analyzer and Quality Classifier

Rahim, Afiq Izzudin A; Ibrahim, Mohd Ismail; Chua, Sook-Ling; Musa, Kamarul Imran

doi:10.3390/healthcare9121679

Cited by 9 publications

(4 citation statements)

References 58 publications

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“…They were 79% accurate in how they put the reviews into classification. The next two studies ( Alayba et al, 2017 ; Rahim et al, 2021 ) used hand-annotated datasets to classify health tweets and got less accurate results. SuryaPrabha & Balakrishnan (2021) , labeled the data with the sentiword-net dictionary, but they also got less accurate results.…”

Section: Resultsmentioning

confidence: 99%

Analyzing patients satisfaction level for medical services using twitter data

Usman,

Mujahid,

Rustam

et al. 2024

PeerJ Computer Science

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Public concern regarding health systems has experienced a rapid surge during the last two years due to the COVID-19 outbreak. Accordingly, medical professionals and health-related institutions reach out to patients and seek feedback to analyze, monitor, and uplift medical services. Such views and perceptions are often shared on social media platforms like Facebook, Instagram, Twitter, etc. Twitter is the most popular and commonly used by the researcher as an online platform for instant access to real-time news, opinions, and discussion. Its trending hashtags (#) and viral content make it an ideal hub for monitoring public opinion on a variety of topics. The tweets are extracted using three hashtags #healthcare, #healthcare services, and #medical facilities. Also, location and tweet sentiment analysis are considered in this study. Several recent studies deployed Twitter datasets using ML and DL models, but the results show lower accuracy. In addition, the studies did not perform extensive comparative analysis and lack validation. This study addresses two research questions: first, what are the sentiments of people toward medical services worldwide? and second, how effective are the machine learning and deep learning approaches for the classification of sentiment on healthcare tweets? Experiments are performed using several well-known machine learning models including support vector machine, logistic regression, Gaussian naive Bayes, extra tree classifier, k nearest neighbor, random forest, decision tree, and AdaBoost. In addition, this study proposes a transfer learning-based LSTM-ETC model that effectively predicts the customer’s satisfaction level from the healthcare dataset. Results indicate that despite the best performance by the ETC model with an 0.88 accuracy score, the proposed model outperforms with a 0.95 accuracy score. Predominantly, the people are happy about the provided medical services as the ratio of the positive sentiments is substantially higher than the negative sentiments. The sentiments, either positive or negative, play a crucial role in making important decisions through customer feedback and enhancing quality.

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Section: Resultsmentioning

confidence: 99%

Analyzing patients satisfaction level for medical services using twitter data

Usman,

Mujahid,

Rustam

et al. 2024

PeerJ Computer Science

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“…In this age of advanced networks, e-patients can access substantial information about healthcare offerings [ 75 ]. In order to respond to problems related to digital healthcare for potential patients and meet the growing need at the highest standards, digital marketing of telemedicine services is needed [ 75 , 76 , 77 ]. Digital healthcare marketing is a component of marketing that uses the Internet and networked technologies (mobile phones, desktop computers, and other devices) to promote telemedicine services.…”

Section: Discussionmentioning

confidence: 99%

Measuring the Impact of Virtual Communities on the Intention to Use Telemedicine Services

Priescu

Oncioiu

2022

Healthcare

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Digital marketing has given new life to healthcare services by enhancing their visibility in the online space. People choose online healthcare services because they can receive instant answers and communicate with specialists in their comfortable environment at the right time. The purpose of this study was to understand the impact of virtual communities on the intention to use telemedicine. The model is based on a combination of consumer desire (psychological objective) and loyalty through promotional formats (economic objective), as well as data collected from 442 respondents analyzed using structural equation modeling. The research results show that by analyzing target groups in social networks, content can be individualized, and an accurate measurement of e-patient satisfaction must be conducted in order to improve the experience of future consumers of telemedicine services. The results of this study explain what makes people want to use digital healthcare services and can serve as a guide for people who run virtual communities and help digital healthcare service providers figure out how to market their services.

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“…In order to train machine learning models, the Python packages (nltk, spacy, and scikit-learn) are utilized, which employ three basic classifiers: Naïve Bayes (NB), support vector machines (SVM), and logistic regression (LR). Several techniques are applied in this study to develop a sentiment analyzer [94][95][96][97][98], which analyzes patient online reviews to classify emerging and fading themes and sentiment trends during the early stage of the COVID-19 outbreak [99].…”

Section: Development Of DL Sentiment Analyzermentioning

confidence: 99%

“…The performance classification of multi-label models is based on the five-fold cross-validation of the ML models[97].…”

mentioning

confidence: 99%

A Review on Electronic Health Record Text-Mining for Biomedical Name Entity Recognition in Healthcare Domain

2023

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Biomedical-named entity recognition (bNER) is critical in biomedical informatics. It identifies biomedical entities with special meanings, such as people, places, and organizations, as predefined semantic types in electronic health records (EHR). bNER is essential for discovering novel knowledge using computational methods and Information Technology. Early bNER systems were configured manually to include domain-specific features and rules. However, these systems were limited in handling the complexity of the biomedical text. Recent advances in deep learning (DL) have led to the development of more powerful bNER systems. DL-based bNER systems can learn the patterns of biomedical text automatically, making them more robust and efficient than traditional rule-based systems. This paper reviews the healthcare domain of bNER, using DL techniques and artificial intelligence in clinical records, for mining treatment prediction. bNER-based tools are categorized systematically and represent the distribution of input, context, and tag (encoder/decoder). Furthermore, to create a labeled dataset for our machine learning sentiment analyzer to analyze the sentiment of a set of tweets, we used a manual coding approach and the multi-task learning method to bias the training signals with domain knowledge inductively. To conclude, we discuss the challenges facing bNER systems and future directions in the healthcare field.

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Hospital Facebook Reviews Analysis Using a Machine Learning Sentiment Analyzer and Quality Classifier

Cited by 9 publications

References 58 publications

Analyzing patients satisfaction level for medical services using twitter data

Analyzing patients satisfaction level for medical services using twitter data

Measuring the Impact of Virtual Communities on the Intention to Use Telemedicine Services

A Review on Electronic Health Record Text-Mining for Biomedical Name Entity Recognition in Healthcare Domain

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