Sentiment Analysis of Health Care: Review

Aattouchi, Issam; Elmendili, Saida; Elmendili, Fatna

doi:10.1051/e3sconf/202131901064

Cited by 9 publications

(7 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…SA, also known as feedback analysis, is a natural language processing process that enables automatic classification of content (opinion) expressed in text [26]. Although the history of SA originates from the 1990s, research on its application has expanded since the emergence of Web 2.0, increased access to information generated by network users, and the proliferation of social media platforms.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Development of decision-making technique based on sentiment analysis of crowdsourcing data in medical social media resources

Mammadova,

Jabrayilova,

Shikhaliyeva

2023

EEJET

View full text Add to dashboard Cite

The object of the study is the decision-making modeling in the context of medical social media to increase the clinics’ effectiveness. The problem is to classify the patient reviews collected in the patient-clinic segment of the medical social media and to identify the situation related to the clinics’ activity by revealing the criteria characterizing the clinics’ activity out of the opinions. The proposed technique refers to lexicon-based sentiment analysis of opinions, the classification based on Valence Aware Dictionary and Sentiment Reasoner (VADER), the verification of the results accuracy with Multinomial Naive Bayes and Support Vector Machine, the manual sentiment analysis of opinions to detect criteria and the classification of opinions according to each criterion. Using this technique, out of 442587 patient reviews obtained from database cms_hospital_satisfaction_2020 of the Kaggle company generated on the basis of crowdsourcing of patient reviews on medical social media, 218914 patient reviews are classified as positive, 190360 – as neutral, and 33313 – as negative. The results accuracy is verified, and the clinics are rated by the «positive» opinions. 6 new criteria characterizing the clinics’ activity are discovered, and the identification of the situation related to the clinics’ activity based on the comparison of «positive» and «negative» opinions according to each criterion is presented. The possibility of using the results obtained from the identification to increase the clinics’ efficiency in making decisions is shown. The results obtained in this study can be used to improve the clinics’ performance according to public opinion. This opportunity involves the crowdsourcing of opinions about the clinic in the medical social media environment and the collection of opinions in a structured way.

show abstract

Section: Methodsmentioning

confidence: 99%

“…Machine learning methods are applied to check the accuracy of the results obtained from SA [26,29]. The study uses Multinomial Naive Bayes (MultinominalNB) and Support Vector Machine (SVM) to check the accuracy of the results obtained from SA.…”

Section: Methodsmentioning

confidence: 99%

Development of decision-making technique based on sentiment analysis of crowdsourcing data in medical social media resources

Mammadova,

Jabrayilova,

Shikhaliyeva

2023

EEJET

View full text Add to dashboard Cite

show abstract

“…In Azerbaijan, social societies such as "Həkim.tap", "həkimsənaz.az", "sağlamolun.az", "doctormap.az", "Tibb.az" bring together the doctors specializing in various fields of medicine on one portal [3,4,7]. Patients may get detailed information about doctors by using the "doctor-search" section in these networks, they can contact those doctors and ask them questions.…”

Section: Methodsmentioning

confidence: 99%

Lexicon-based sentiment analysis of medical data

Mammadova

Jabrayilova

Shikhaliyeva

2022

TT: PhE

View full text Add to dashboard Cite

The article explores the possibilities of applying sentiment analysis for the use of information collected in the medical social media environment in medical decision-making. Opinions and feedbacks of medical social media subjects (physician, patient, health institution, etc.) make media resources an important source of information. The information collected in these sources can be used to improve the quality of health care and make decisions, taking into account the public opinion. Researches in this field have actualized the application of artificial intelligence methods, i.e., sentiment analysis methods. In this regard, it segments the medical social media environment in accordance with user relationships, and shows the nature of the information collected on each segment and its importance in decision-making to improve the quality of medical services. The possibilities of applying the lexicon-based sentiment analysis method for studying and classifying the collected data are explained in detail. The open database cms_hospital_satisfaction_2019 by the Kaggle company is used, and the opinions collected from patients about the services provided by a specific medical center are analyzed. This study analyzes opinions using the Valence Aware Dictionary and Sentiment Reasoner lexicon and classifies them as neutral, positive and negative and the implementation of this process is described in stages. The importance of the obtained results in decision-making regarding the better organization, evaluation and improvement of the activity of the medical institution is shown

show abstract

“…Although various forms of patient feedback practices exist, their use is found to be limited to hospital administration and management contexts [ 3 ]. Accordingly, sentiment analysis is effective in providing insights into how strongly opinionated the patient feedback is, whilst providing the healthcare providers with the customer’s opinion about their product or service [ 4 , 5 ]. There are currently a wide range of sentiment analysis studies that are focused on the synthesis of data from websites and various social media platforms such as Twitter, Facebook, etc.…”

Section: Introductionmentioning

confidence: 99%

Advanced Sentiment Analysis for Managing and Improving Patient Experience: Application for General Practitioner (GP) Classification in Northamptonshire

Pandey,

Seify,

Okonta

et al. 2023

IJERPH

View full text Add to dashboard Cite

This paper presents a novel analytical approach for improving patients’ experience in healthcare settings. The analytical tool uses a classifier and a recommend management approach to facilitate decision making in a timely manner. The designed methodology comprises of 4 key stages, which include developing a bot to scrap web data while performing sentiment analysis and extracting keywords from National Health Service (NHS) rate and review webpages, building a classifier with Waikato Environment for Knowledge Analysis (WEKA), analyzing speech with Python, and using Microsoft Excel for analysis. In the selected context, a total of 178 reviews were extracted from General Practitioners (GP) websites within Northamptonshire County, UK. Accordingly, 4764 keywords such as “kind”, “exactly”, “discharged”, “long waits”, “impolite staff”, “worse”, “problem”, “happy”, “late” and “excellent” were selected. In addition, 178 reviews were analyzed to highlight trends and patterns. The classifier model grouped GPs into gold, silver, and bronze categories. The outlined analytical approach complements the current patient feedback analysis approaches by GPs. This paper solely relied upon the feedback available on the NHS’ rate and review webpages. The contribution of the paper is to highlight the integration of easily available tools to perform higher level of analysis that provides understanding about patients’ experience. The context and tools used in this study for ranking services within the healthcare domain is novel in nature, since it involves extracting useful insights from the provided feedback.

show abstract

Sentiment Analysis of Health Care: Review

Cited by 9 publications

References 26 publications

Development of decision-making technique based on sentiment analysis of crowdsourcing data in medical social media resources

Development of decision-making technique based on sentiment analysis of crowdsourcing data in medical social media resources

Lexicon-based sentiment analysis of medical data

Advanced Sentiment Analysis for Managing and Improving Patient Experience: Application for General Practitioner (GP) Classification in Northamptonshire

Contact Info

Product

Resources

About