Health and Quality of Life

Berg, Ole Johan

doi:10.1177/000169937501800102

Cited by 22 publications

(3 citation statements)

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“…This makes well-being an ideal case study for SA. However, when it comes to matters of health, modern society tends to be preoccupied with the negative phenomena such as diseases, injuries, and disabilities [11], which makes SA in this domain challenging. For instance, for a patient with a chronic condition, having a good quality of life will not necessarily depend on the absence of associated symptoms, but rather on the extent to which they are managed and controlled.…”

Section: Introductionmentioning

confidence: 99%

Sentiment Analysis in Health and Well-Being: Systematic Review

Žunić¹,

Corcoran²,

Spasić³

2020

JMIR Med Inform

121

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Background Sentiment analysis (SA) is a subfield of natural language processing whose aim is to automatically classify the sentiment expressed in a free text. It has found practical applications across a wide range of societal contexts including marketing, economy, and politics. This review focuses specifically on applications related to health, which is defined as “a state of complete physical, mental, and social well-being and not merely the absence of disease or infirmity.” Objective This study aimed to establish the state of the art in SA related to health and well-being by conducting a systematic review of the recent literature. To capture the perspective of those individuals whose health and well-being are affected, we focused specifically on spontaneously generated content and not necessarily that of health care professionals. Methods Our methodology is based on the guidelines for performing systematic reviews. In January 2019, we used PubMed, a multifaceted interface, to perform a literature search against MEDLINE. We identified a total of 86 relevant studies and extracted data about the datasets analyzed, discourse topics, data creators, downstream applications, algorithms used, and their evaluation. Results The majority of data were collected from social networking and Web-based retailing platforms. The primary purpose of online conversations is to exchange information and provide social support online. These communities tend to form around health conditions with high severity and chronicity rates. Different treatments and services discussed include medications, vaccination, surgery, orthodontic services, individual physicians, and health care services in general. We identified 5 roles with respect to health and well-being among the authors of the types of spontaneously generated narratives considered in this review: a sufferer, an addict, a patient, a carer, and a suicide victim. Out of 86 studies considered, only 4 reported the demographic characteristics. A wide range of methods were used to perform SA. Most common choices included support vector machines, naïve Bayesian learning, decision trees, logistic regression, and adaptive boosting. In contrast with general trends in SA research, only 1 study used deep learning. The performance lags behind the state of the art achieved in other domains when measured by F-score, which was found to be below 60% on average. In the context of SA, the domain of health and well-being was found to be resource poor: few domain-specific corpora and lexica are shared publicly for research purposes. Conclusions SA results in the area of health and well-being lag behind those in other domains. It is yet unclear if this is because of the intrinsic differences between the domains and their respective sublanguages, the size of training datasets, the lack of domain-specific sentiment lexica, or the choice of algorithms.

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Section: Introductionmentioning

confidence: 99%

Sentiment Analysis in Health and Well-Being: Systematic Review

Žunić¹,

Corcoran²,

Spasić³

2020

JMIR Med Inform

121

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“…Finally, the high accuracy the model achieved in the realm of health and well-being opens up an array of possible applications in this domain [49]. When it comes to health, modern society tends to be preoccupied with inherently negative phenomena, such as diseases, injuries and disabilities [50]. However, for chronic patients, achieving a good quality of life does not necessarily imply the absence of symptoms that are associated with their medical condition.…”

Section: Discussionmentioning

confidence: 99%

The Case of Aspect in Sentiment Analysis: Seeking Attention or Co-Dependency?

Žunić

Corcoran

Spasić

2022

MAKE

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(1) Background: Aspect-based sentiment analysis (SA) is a natural language processing task, the aim of which is to classify the sentiment associated with a specific aspect of a written text. The performance of SA methods applied to texts related to health and well-being lags behind that of other domains. (2) Methods: In this study, we present an approach to aspect-based SA of drug reviews. Specifically, we analysed signs and symptoms, which were extracted automatically using the Unified Medical Language System. This information was then passed onto the BERT language model, which was extended by two layers to fine-tune the model for aspect-based SA. The interpretability of the model was analysed using an axiomatic attribution method. We performed a correlation analysis between the attribution scores and syntactic dependencies. (3) Results: Our fine-tuned model achieved accuracy of approximately 95% on a well-balanced test set. It outperformed our previous approach, which used syntactic information to guide the operation of a neural network and achieved an accuracy of approximately 82%. (4) Conclusions: We demonstrated that a BERT-based model of SA overcomes the negative bias associated with health-related aspects and closes the performance gap against the state-of-the-art in other domains.

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“…Regarding health issues, of course, Because of this, wellbeing is an excellent case study for SA. Of course, when it comes to health issues, modern society is focused on negative occurrences like illness, damage, and incapacity [11], making SA difficult in this field. For example, having a good quality of life for a patient with a long-term illness will depend less on the absence of accompanying problems and more on how well they are controlled and managed.…”

Section: Introductionmentioning

confidence: 99%

Sentiment Analysis of Health Care: Review

Aattouchi

Elmendili

2021

E3S Web Conf.

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Twitter is a microblogging service where users can send and read short messages of 140 characters called “tweets”. Many healthcare-related unstructured and free-text tweets are shared on Twitter, which is becoming a popular domain for medical research. Sentiment analysis is one of the data mining types that provides an estimate of the direction of personality sentiment analysis in natural language processing. By analyzing text, computational linguistics is used to infer and analyze mental knowledge of the web, social media, and related references. The data reviewed actually quantifies the attitudes or feelings of the global society towards specific goods, people, or thoughts and exposes the contextual duality of the knowledge. Sentiment analysis is used in various sectors such as health care. There is an incredible amount of healthcare information available online, such as social media, and websites focused on rating medical problems, that is not accessed in a methodical way. Sentiment analysis has many benefits, such as using medical information to achieve the best possible patient outcome and improve the quality of health care. This review paper focuses on the presented sentiment analysis methods that are used in the medical field.

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Health and Quality of Life

Cited by 22 publications

References 0 publications

Sentiment Analysis in Health and Well-Being: Systematic Review

Sentiment Analysis in Health and Well-Being: Systematic Review

The Case of Aspect in Sentiment Analysis: Seeking Attention or Co-Dependency?

Sentiment Analysis of Health Care: Review

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