Abstract
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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Author Keywords
- Coronavirus,
- COVID-19,
- Natural Language Processing,
- Topic modeling,
- Deep Learning
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IEEE Keywords
- Semantics,
- Natural language processing,
- Social networking (online) ,
- Viruses (medical) ,
- Computational modeling
Introduction
discussion forums, such as Reddit, enable healthcare service providers to collect people/patient experience data. These forums are valuable sources of people’s opinions, which can be examined for knowledge discovery and user behavior analysis. In a typical subreddit forum, a user can use keywords and apply search tools to identify relevant questions/answers or comments sent in by other Reddit users. Moreover, a registered user can create a topic or post new questions to start discussions with other community members. Other users can reflect and share their views and experiences in response to each of the questions. In these online forums, people may express their positive and negative comments, or share questions, problems, and needs related to health issues. By analyzing these comments, we can identify valuable recommendations for improving health services and understanding the problems of users.
In late December 2019, the outbreak of a novel coronavirus causing COVID-19 was reported [1]. Due to the rapid spread of the virus, 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. Our investigation was guided by the following specific research questions (RQ): RQ1) How can important concepts in NLP methods such as topic modeling be applied in online discussions to uncover various issues related to COVID-19 from public opinions? RQ2) How can we obtain the sentiment polarity of the COVID-19 comments posted by users reflecting their opinions? RQ3) What is the comparative performance of various machine-learning algorithms for sentiment classification of COVID-19 online discussions, and which classification algorithm performs better?
To address the above questions, we focused on analyzing COVID-19–related comments to detect sentiment and semantic ideas relating to COVID-19 based on the public opinions of people on Reddit. Specifically, we used the 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. The main contributions of this paper are as follows: · We present a systematic framework based on NLP that is capable of extracting meaningful topics from COVID19–related comments on Reddit. · We propose a deep learning model based on Long ShortTerm Memory (LSTM) for sentiment classification of COVID-19–related comments, which produces better results compared with several other well-known machine-learning methods. · We detect and uncover meaningful topics that are being discussed on COVID-19–related issues on Reddit, as primary research. · We calculate the polarity of the COVID-19 comments related to sentiment and opinion analysis from 10 subreddits. 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 decisionmaking. Overall, the paper is structured as follows. First, we provide a brief introduction to online healthcare forums. Discussion of COVID-19–related issues and some similar works are provided in section 2. In section 3, we describe the data pre-processing methods adopted in our research, and the NLP and deep-learning methods applied to the COVID-19 comments database. Next, we present the results and discussion. Finally, we conclude and discuss future works based on NLP approaches for analyzing the online community in relation to the topic of COVID-19.
Conclusion
To our knowledge, this is the first study to analyze the association between COVID-19 comments’ sentiment and semantic topics on Reddit. The main goal of this paper, however, was to show a novel application for NLP based on an LSTM model to detect meaningful latent-topics and sentiment-comment-classification on COVID-19–related issues from healthcare forums, such as subreddits. We believe that the results of this paper will aid in understanding the concerns and needs of people with respect to COVID-19– related issues. Moreover, our findings may aid in improving practical strategies for public health services and interventions related to COVID-19.
Acknowledgment
We acknowledge SciTechEdit International, LLC (Highlands Ranch, CO, USA) for providing pro bono professional English-language editing of this article.
About KSRA
The Kavian Scientific Research Association (KSRA) is a non-profit research organization to provide research / educational services in December 2013. The members of the community had formed a virtual group on the Viber social network. The core of the Kavian Scientific Association was formed with these members as founders. These individuals, led by Professor Siavosh Kaviani, decided to launch a scientific / research association with an emphasis on education.
KSRA research association, as a non-profit research firm, is committed to providing research services in the field of knowledge. The main beneficiaries of this association are public or private knowledge-based companies, students, researchers, researchers, professors, universities, and industrial and semi-industrial centers around the world.
Our main services Based on Education for all Spectrum people in the world. We want to make an integration between researches and educations. We believe education is the main right of Human beings. So our services should be concentrated on inclusive education.
The KSRA team partners with local under-served communities around the world to improve the access to and quality of knowledge based on education, amplify and augment learning programs where they exist, and create new opportunities for e-learning where traditional education systems are lacking or non-existent.
FULL Paper PDF file:
Deep Sentiment Classification and Topic Discovery on Novel Coronavirus or COVID-19 Online Discussions: NLP Using LSTM Recurrent Neural Network ApproachBibliography
author
Year
2020
Title
Deep Sentiment Classification and Topic Discovery on Novel Coronavirus or COVID-19 Online Discussions: NLP Using LSTM Recurrent Neural Network Approach
Publish in
in the IEEE Journal of Biomedical and Health Informatics,
Doi
10.1109/JBHI.2020.3001216
PDF reference and original file: Click here
Somayeh Nosrati was born in 1982 in Tehran. She holds a Master's degree in artificial intelligence from Khatam University of Tehran.
Professor Siavosh Kaviani was born in 1961 in Tehran. He had a professorship. He holds a Ph.D. in Software Engineering from the QL University of Software Development Methodology and an honorary Ph.D. from the University of Chelsea.
Nasim Gazerani was born in 1983 in Arak. She holds a Master's degree in Software Engineering from UM University of Malaysia.
