Use of Sentiment Analysis Techniques in Healthcare Domain

Ramírez-Tinoco, Francisco Javier; Alor-Hernández, Giner; Sánchez-Cervantes, José Luis; Salas-Zárate, María del Pilar; Valencia‐García, Rafael

doi:10.1007/978-3-030-06149-4_8

Cited by 23 publications

(8 citation statements)

References 21 publications

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“…Thus, mining the sentiments expressed in drug reviews can be used for the effective evaluation of drugs. Sentiment analysis has been an important research area for the last decade concerning the wide use of web platforms and availability of users' reviews [4]. Sentiment analysis also called opinion mining focuses on the identification of a user's attitude towards an entity or overall sentiment of a text into positive, negative, or other sentiments [5].…”

Section: Introductionmentioning

confidence: 99%

Determining the Efficiency of Drugs Under Special Conditions From Users’ Reviews on Healthcare Web Forums

et al. 2021

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Sentiment analysis is the extraction and categorization of sentiments that have been expressed in text data using text analysis techniques. Manifested by earlier studies, sentiment analysis of drug reviews has a large potential for providing valuable insights to assist health-care professionals and companies for evaluating the safety of drugs after it has been marketed. Such insights help safeguard patients and increase their trust in medical companies. The existing systems either follow a lexicon-based approach or a learningbased approach for sentiment analysis in the medical domain. Learning-based techniques require annotated data while lexicon-based techniques tend to be domain-specific which restricts their wide use. This research embarks on a hybrid technique that utilizes both learning-based and lexicon-based approaches to achieve better results. General-purpose sentiment lexicons, such as AFFIN, TextBlob, and VADER, are used for annotating the reviews. Furthermore, several feature engineering techniques, such as term frequency (TF), term frequency-inverse document frequency (TF-IDF), and union of TF and TF-IDF (TF U TF-IDF) have been incorporated for the extraction of useful features. Finally, the learning models including logistic regression (LR), AdaBoost classifier (AB), random forest (RF), extra tree classifier (ETC), and multilayer perceptron (MLP) are used to classify sentiments of the reviews. The performance of the proposed hybrid approach is evaluated using accuracy, precision, recall, and F1-score. Experimental results indicate that the combination of learning-based and lexicon-based approaches provide improved results than their individual use. Moreover, TextBlob has shown promising results giving an accuracy of 96% with MLP when used with TF-IDF and with LR when used with TF U TF-IDF.

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Section: Introductionmentioning

confidence: 99%

Determining the Efficiency of Drugs Under Special Conditions From Users’ Reviews on Healthcare Web Forums

et al. 2021

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“…The Healthcare domain is taking so much interest in SA recently. This allows medical actors to extract information about drug reactions, disease diagnosis, epidemics, and patient moods (Ramírez-Tinoco et al 2019;Tiwari et al 2021).…”

Section: Technical Discussionmentioning

confidence: 99%

A systematic review of social network sentiment analysis with comparative study of ensemble-based techniques

et al. 2023

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Sentiment Analysis (SA) of text reviews is an emerging concern in Natural Language Processing (NLP). It is a broadly active method for analyzing and extracting opinions from text using individual or ensemble learning techniques. This field has unquestionable potential in the digital world and social media platforms. Therefore, we present a systematic survey that organizes and describes the current scenario of the SA and provides a structured overview of proposed approaches from traditional to advance. This work also discusses the SA-related challenges, feature engineering techniques, benchmark datasets, popular publication platforms, and best algorithms to advance the automatic SA. Furthermore, a comparative study has been conducted to assess the performance of bagging and boosting-based ensemble techniques for social network SA. Bagging and Boosting are two major approaches of ensemble learning that contain various ensemble algorithms to classify sentiment polarity. Recent studies recommend that ensemble learning techniques have the potential of applicability for sentiment classification. This analytical study examines the bagging and boosting-based ensemble techniques on four benchmark datasets to provide extensive knowledge regarding ensemble techniques for SA. The efficiency and accuracy of these techniques have been measured in terms of TPR, FPR, Weighted F-Score, Weighted Precision, Weighted Recall, Accuracy, ROC-AUC curve, and Run-Time. Moreover, comparative results reveal that bagging-based ensemble techniques outperformed boosting-based techniques for text classification. This extensive review aims to present benchmark information regarding social network SA that will be helpful for future research in this field.

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“…However, in order for various stakeholders to profit from the knowledge gleaned through sentiment analysis, a study by [16] proposes the use of sentiment analysis of hybrid techniques through the development of a module that makes it possible to integrate sentiment analysis functionalities in Web applications related to healthcare at the comment and entity levels. The healthcare industry is one of the least researched industries, however, researchers were aware of a few instances where sentiment analysis was applied to help various industries.…”

Section: B Sentiment Analysis On Healthcare Servicesmentioning

confidence: 99%

Assessment of the Healthcare Administration of Senior Citizens from Survey Data using Sentiment Analysis

Magtangob¹,

Palaoag²

2023

IJACSA

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Healthcare is most frequently used by older people and understanding how they feel about the way healthcare administration gives them the attention and support they need, is crucial to building a healthcare system that is effective in meeting their needs. This study determined the seniors' opinions on the healthcare administration by employing SurveyMonkey, a robust online survey tool as an opinion miner. The study used the Orange application, which made data processing simple, to gauge the seniors' opinions toward healthcare administration by analyzing text sentiment using the VADER Sentiment Analysis, which may distinguish between the polarity of positive, negative, or neutral emotions as well as their intensity. Results showed that the majority of seniors (51.1%) had a negative response to healthcare administration, whereas 47.9% had a neutral response and 1.0% had a positive response. Based on the study, the government should enhance its senior citizens' healthcare services to better satisfy their demands and ensure their happiness. This is clear from the respondents' feedback regarding the services they would like to utilize and how they believe they may be improved. Additionally, the findings provided sufficient information for future consideration to enhance seniors' satisfaction with developmental activities and programs and improve healthcare administration.

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Use of Sentiment Analysis Techniques in Healthcare Domain

Cited by 23 publications

References 21 publications

Determining the Efficiency of Drugs Under Special Conditions From Users’ Reviews on Healthcare Web Forums

Determining the Efficiency of Drugs Under Special Conditions From Users’ Reviews on Healthcare Web Forums

A systematic review of social network sentiment analysis with comparative study of ensemble-based techniques

Assessment of the Healthcare Administration of Senior Citizens from Survey Data using Sentiment Analysis

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