Similarity of medical concepts in question and answering of health communities

Naderi, Hassan; Madani, Sina; Kiani, Behzad; Etminani, Kobra

doi:10.1177/1460458219881333

“…Machine and deep-learning approaches based on sentiment and semantic analysis are popular methods of analysing textcontent in online health forums. Many researchers have used these methods on social media such as Twitter, reddit [2]- [7], and health information websites [8], [9]. For example; Halder and colleagues [10] focused on exploring linguistic changes to analyse the emotional status of a user over time.…”

Section: Related Workmentioning

confidence: 99%

Deep Sentiment Classification and Topic Discovery on Novel Coronavirus or COVID-19 Online Discussions: NLP Using LSTM Recurrent Neural Network Approach

Jelodar

¹

,

Wang

²

,

Orji

³

et al. 2020

IEEE J. Biomed. Health Inform.

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Internet forums and public social media, such as online healthcare forums, provide a convenient channel for users (people/patients) concerned about health issues to discuss and share information with each other. In late December 2019, an outbreak of a novel coronavirus (infection from which results in the disease named COVID-19) was reported, and, due to the rapid spread of the virus in other parts of the world, the World Health Organization declared a state of emergency. In this paper, we used automated extraction of COVID-19-related discussions from social media and a natural language process (NLP) method based on topic modeling to uncover various issues related to COVID-19 from public opinions. Moreover, we also investigate how to use LSTM recurrent neural network for sentiment classification of COVID-19 comments. Our findings shed light on the importance of using public opinions and suitable computational techniques to understand issues surrounding COVID-19 and to guide related decision-making. In addition, experiments demonstrated that the research model achieved an accuracy of 81.15%-a higher accuracy than that of several other well-known machine-learning algorithms for COVID-19-Sentiment Classification.

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“…20 These websites provide a venue for consumers to seek experiential information from other users for a quick solution to their healthcare concerns. 21 It has previously been observed that Q&A platforms offer a valuable opportunity to better understand users' information needs and concerns about various health issues. 22 Text mining has been introduced as a useful method for studying the health information needs in Q&A forums from different perspectives.…”

Section: Health Information Needs On Question-and-answer Forumsmentioning

confidence: 99%

Understanding information needs during COVID-19: A comparison study between an online health community and a Q&A platform

Peng

¹

,

Wang

²

2022

Health Informatics J

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This paper aims at identifying user’s information needs on Coronavirus and the differences of user’s information needs between the online health community MedHelp and the question-and-answer forum Quora during the COVID-19 global pandemic. We obtained the posts in the sub-community Coronavirus on MedHelp (195 posts with 1627 answers) and under the topic of COVID-19(2019-2020) on Quora (263 posts with 8401 answers) via web scraping built on Selenium WebDriver. After preprocessing, we conducted topic modeling on both corpora and identified the best topic model for each corpus based on the diagnostic metrics. Leveraging the improved sqrt-cosine similarity measurement, we further compared the topic similarity between these two corpora. This study finds that there are common information needs on both platforms about vaccination and the essential elements of the disease including the onset symptoms, transmission routes, preventive measures, treatment and control of COVID-19. Some unique discussions on MedHelp are about psychological health, and therapeutic management of patients. Users on Quora have special interests of information about the association between vaccine and Luciferase, and attacks on Fauci after email trove released. The work is beneficial for researchers who aim to provide accurate information assistance and build effective online emergence response programs during the pandemic.

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“…Machine and deep-learning approaches based on sentiment and semantic analysis are popular methods of analysing text-content in online health forums. Many researchers have used these methods on social media such as Twitter, reddit [2] - [7], and health information websites [8], [9]. For example; Halder and colleagues [10] focused on exploring linguistic changes to analyse the emotional status of a user over time.…”

Section: Introductionmentioning

confidence: 99%

Deep Sentiment Classification and Topic Discovery on Novel Coronavirus or COVID-19 Online Discussions: NLP Using LSTM Recurrent Neural Network Approach

Jelodar

¹

,

Wang

²

,

Orji

³

2020

Preprint

View full text Add to dashboard Cite

Internet forums and public social media, such as online healthcare forums, provide a convenient channel for users (people/patients) concerned about health issues to discuss and share information with each other. In late December 2019, an outbreak of a novel coronavirus (infection from which results in the disease named COVID-19) was reported, and, due to the rapid spread of the virus in other parts of the world, the World Health Organization declared a state of emergency. In this paper, we used automated extraction of COVID-19-related discussions from social media and a natural language process (NLP) method based on topic modeling to uncover various issues related to COVID-19 from public opinions. Moreover, we also investigate how to use LSTM recurrent neural network for sentiment classification of COVID-19 comments. Our findings shed light on the importance of using public opinions and suitable computational techniques to understand issues surrounding COVID-19 and to guide related decision-making.

show abstract

Similarity of medical concepts in question and answering of health communities

Cited by 7 publications

References 20 publications

Deep Sentiment Classification and Topic Discovery on Novel Coronavirus or COVID-19 Online Discussions: NLP Using LSTM Recurrent Neural Network Approach

Deep Sentiment Classification and Topic Discovery on Novel Coronavirus or COVID-19 Online Discussions: NLP Using LSTM Recurrent Neural Network Approach

Understanding information needs during COVID-19: A comparison study between an online health community and a Q&A platform

Deep Sentiment Classification and Topic Discovery on Novel Coronavirus or COVID-19 Online Discussions: NLP Using LSTM Recurrent Neural Network Approach

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