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Charquero-Ballester M, Walter JG, Rybner AS, Nissen IA, Enevoldsen KC, Bechmann A. Emotions on Twitter as crisis imprint in high-trust societies: Do ambient affiliations affect emotional expression during the pandemic? PLoS One 2024; 19:e0296801. [PMID: 38442085 PMCID: PMC10914277 DOI: 10.1371/journal.pone.0296801] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 12/19/2023] [Indexed: 03/07/2024] Open
Abstract
During the Covid-19 crisis, citizens turned to Twitter for information seeking, emotional outlet and sense-making of the crisis, creating ad hoc social communities using crisis-specific hashtags. The theory of ambient affiliation posits that the use of hashtags upscales the call to affiliate with the values expressed in the tweet. Given the deep functional tie between values and emotions, hashtag use might further amplify certain emotions. While emotions in crises-hashtagged communities have been previously investigated, the hypothesis of amplification of emotions through hashtag use has not yet been tested. We investigate such effect during the Covid-19 crisis in a scenario of high-trust Nordic societies, focusing on non-hashtagged, crisis hashtagged (e.g., '#Covid-19') and threat hashtagged (e.g., '#misinformation') tweets. To do so we apply XLM-RoBERTa to estimate Anger, Fear, Sadness, Disgust, Joy and Optimism. Our results revealed that crisis-hashtagged (#Covid-19) tweets expressed more negative emotions (Anger, Fear, Disgust and Sadness) and less positive emotions (Optimism and Joy) than non-hashtagged Covid-19 tweets for all countries except Finland. Threat tweets (#misinformation) expressed even more negative emotions (Anger, Fear, Disgust) and less positive emotions (Optimism and Joy) than #Covid-19 tweets, with a particularly large effect for Anger. Our findings provide useful context for previous research on collective emotions during crises, as most Twitter content is not hashtagged, and given the faster spread of emotionally charged content, further support the special focus on specific ad hoc communities for crisis and threat management and monitoring.
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Affiliation(s)
- Marina Charquero-Ballester
- Department of Media and Journalism Studies, School of Communication and Culture, Aarhus University, Aarhus, Denmark
| | - Jessica Gabriele Walter
- Department of Media and Journalism Studies, School of Communication and Culture, Aarhus University, Aarhus, Denmark
| | - Astrid Sletten Rybner
- Department of Media and Journalism Studies, School of Communication and Culture, Aarhus University, Aarhus, Denmark
| | - Ida Anthonj Nissen
- Department of Media and Journalism Studies, School of Communication and Culture, Aarhus University, Aarhus, Denmark
| | | | - Anja Bechmann
- Department of Media and Journalism Studies, School of Communication and Culture, Aarhus University, Aarhus, Denmark
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2
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Thakur D, Gera T, Bhardwaj V, AlZubi AA, Ali F, Singh J. An enhanced diabetes prediction amidst COVID-19 using ensemble models. Front Public Health 2023; 11:1331517. [PMID: 38155892 PMCID: PMC10754515 DOI: 10.3389/fpubh.2023.1331517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 11/21/2023] [Indexed: 12/30/2023] Open
Abstract
In the contemporary landscape of healthcare, the early and accurate prediction of diabetes has garnered paramount importance, especially in the wake of the COVID-19 pandemic where individuals with diabetes exhibit increased vulnerability. This research embarked on a mission to enhance diabetes prediction by employing state-of-the-art machine learning techniques. Initial evaluations highlighted the Support Vector Machines (SVM) classifier as a promising candidate with an accuracy of 76.62%. To further optimize predictions, the study delved into advanced feature engineering techniques, generating interaction and polynomial features that unearthed hidden patterns in the data. Subsequent correlation analyses, visualized through heatmaps, revealed significant correlations, especially with attributes like Glucose. By integrating the strengths of Decision Trees, Gradient Boosting, and SVM in an ensemble model, we achieved an accuracy of 93.2%, showcasing the potential of harmonizing diverse algorithms. This research offers a robust blueprint for diabetes prediction, holding profound implications for early diagnosis, personalized treatments, and preventive care in the context of global health challenges and with the goal of increasing life expectancy.
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Affiliation(s)
- Deepak Thakur
- Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India
| | - Tanya Gera
- Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India
| | - Vivek Bhardwaj
- Department of Computer Science and Engineering, Manipal University Jaipur, Jaipur, Rajasthan
| | - Ahmad Ali AlZubi
- Department of Computer Science, Community College, King Saud University, Riyadh, Saudi Arabia
| | - Farman Ali
- Department of Computer Science and Engineering, School of Convergence, College of Computing and Informatics, Sungkyunkwan University, Seoul, Republic of Korea
| | - Jaiteg Singh
- Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India
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3
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Córdoba-Cabús A, García-Borrego M, Ceballos Y. Sentiment Analysis toward the COVID-19 Vaccine in the Main Latin American Media on Twitter: The Cases of Argentina, Chile, Colombia, Mexico, and Peru. Vaccines (Basel) 2023; 11:1592. [PMID: 37896994 PMCID: PMC10610635 DOI: 10.3390/vaccines11101592] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 10/09/2023] [Accepted: 10/12/2023] [Indexed: 10/29/2023] Open
Abstract
This article analyzes the media coverage of the COVID-19 vaccine by major media outlets in five Latin American countries: Argentina, Colombia, Chile, Mexico, and Peru. For this purpose, the XLM-roBERTa model was applied and the sentiments of all tweets published between January 2020 and June 2023 (n = 24,243) by the five outlets with the greatest online reach in each country were analyzed. The results show that the sentiment in the overall media and in each nation studied was mostly negative, and only at the beginning of the pandemic was there some positivity. In recent months, negative sentiment has increased twelvefold over positive sentiment, and has also garnered many more interactions than positive sentiment. The differences by platform and country are minimal, but there are markedly negative media, some more inclined to neutrality, and only one where positive sentiment predominates. This paper questions the role of journalism in Latin America during a health crisis as serious as that of the coronavirus, in which, instead of the expected neutrality, or even a certain message of hope, the media seem to have been dragged along by the negativity promoted by certain discourses far removed from scientific evidence.
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Affiliation(s)
- Alba Córdoba-Cabús
- Department of Journalism, Faculty of Communication Sciences, University of Malaga, 29071 Málaga, Spain; (M.G.-B.); (Y.C.)
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Malik A, Shabaz M, Asenso E. Machine learning based model for detecting depression during Covid-19 crisis. SCIENTIFIC AFRICAN 2023; 20:e01716. [PMID: 37214195 PMCID: PMC10182866 DOI: 10.1016/j.sciaf.2023.e01716] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Revised: 04/14/2023] [Accepted: 05/12/2023] [Indexed: 05/24/2023] Open
Abstract
Covid-19 has impacted negatively on people all over the world. Some of the ways that it has affected people include such as Health, Employment, Mental Health, Education, Social isolation, Economic Inequality and Access to healthcare and essential services. Apart from physical symptoms, it has caused considerable damage to mental health of individuals. Among all, depression is identified as one of the common illnesses which leads to early death. People suffering from depression are at a higher risk of developing other health conditions, such as heart disease and stroke, and are also at a higher risk of suicide. The importance of early detection and intervention of depression cannot be overstated. Identifying and treating depression early can prevent the illness from becoming more severe and can also prevent the development of other health conditions. Early detection can also prevent suicide, which is a leading cause of death among people with depression. Millions of people have affected from this disease. To proceed with the study of depression detection among individuals we have conducted a survey with 21 questions based on Hamilton tool and advise of psychiatrist. With the use of Python's scientific programming principles and machine learning methods like Decision Tree, KNN, and Naive Bayes, survey results were analysed. Further a comparison of these techniques is done. Study concludes that KNN has given better results than other techniques based on the accuracy and decision tree has given better results in the terms of latency to detect the depression of a person. At the conclusion, a machine learning-based model is suggested to replace the conventional method of detecting sadness by asking people encouraging questions and getting regular feedback from them.
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Affiliation(s)
- Arun Malik
- School of Computer Science and Engineering, Lovely Professional University, Phagwara, Punjab 144411, India
| | - Mohammad Shabaz
- Model Institute of Engineering and Technology Jammu, J&K, India
| | - Evans Asenso
- Department of Agricultural Engineering, School of Engineering Sciences, University of Ghana, Accra, Ghana
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5
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Aslan S, Kızıloluk S, Sert E. TSA-CNN-AOA: Twitter sentiment analysis using CNN optimized via arithmetic optimization algorithm. Neural Comput Appl 2023; 35:10311-10328. [PMID: 36714074 PMCID: PMC9867606 DOI: 10.1007/s00521-023-08236-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 01/06/2023] [Indexed: 01/21/2023]
Abstract
COVID-19, a novel virus from the coronavirus family, broke out in Wuhan city of China and spread all over the world, killing more than 5.5 million people. The speed of spreading is still critical as an infectious disease, and it causes more and more deaths each passing day. COVID-19 pandemic has resulted in many different psychological effects on people's mental states, such as anxiety, fear, and similar complex feelings. Millions of people worldwide have shared their opinions on COVID-19 on several social media websites, particularly on Twitter. Therefore, it is likely to minimize the negative psychological impact of the disease on society by obtaining individuals' views on COVID-19 from social media platforms, making deductions from their statements, and identifying negative statements about the disease. In this respect, Twitter sentiment analysis (TSA), a recently popular research topic, is used to perform data analysis on social media platforms such as Twitter and reach certain conclusions. The present study, too, proposes TSA using convolutional neural network optimized via arithmetic optimization algorithm (TSA-CNN-AOA) approach. Firstly, using a designed API, 173,638 tweets about COVID-19 were extracted from Twitter between July 25, 2020, and August 30, 2020 to create a database. Later, significant information was extracted from this database using FastText Skip-gram. The proposed approach benefits from a designed convolutional neural network (CNN) model as a feature extractor. Thanks to arithmetic optimization algorithm (AOA), a feature selection process was also applied to the features obtained from CNN. Later, K-nearest neighbors (KNN), support vector machine, and decision tree were used to classify tweets as positive, negative, and neutral. In order to measure the TSA performance of the proposed method, it was compared with different approaches. The results demonstrated that TSA-CNN-AOA (KNN) achieved the highest tweet classification performance with an accuracy rate of 95.098. It is evident from the experimental studies that the proposed approach displayed a much higher TSA performance compared to other similar approaches in the existing literature.
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Affiliation(s)
- Serpil Aslan
- Department of Software Engineering, Faculty of Engineering and Natural Sciences, Malatya Turgut Ozal University, 44210 Malatya, Turkey
| | - Soner Kızıloluk
- Department of Computer Engineering, Faculty of Engineering and Natural Sciences, Malatya Turgut Ozal University, 44210 Malatya, Turkey
| | - Eser Sert
- Department of Computer Engineering, Faculty of Engineering and Natural Sciences, Malatya Turgut Ozal University, 44210 Malatya, Turkey
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Agrawal S, Jain SK, Sharma S, Khatri A. COVID-19 Public Opinion: A Twitter Healthcare Data Processing Using Machine Learning Methodologies. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 20:432. [PMID: 36612755 PMCID: PMC9819913 DOI: 10.3390/ijerph20010432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 12/20/2022] [Accepted: 12/22/2022] [Indexed: 06/17/2023]
Abstract
The COVID-19 pandemic has shattered the whole world, and due to this, millions of people have posted their sentiments toward the pandemic on different social media platforms. This resulted in a huge information flow on social media and attracted many research studies aimed at extracting useful information to understand the sentiments. This paper analyses data imported from the Twitter API for the healthcare sector, emphasizing sub-domains, such as vaccines, post-COVID-19 health issues and healthcare service providers. The main objective of this research is to analyze machine learning models for classifying the sentiments of people and analyzing the direction of polarity by considering the views of the majority of people. The inferences drawn from this analysis may be useful for concerned authorities as they work to make appropriate policy decisions and strategic decisions. Various machine learning models were developed to extract the actual emotions, and results show that the support vector machine model outperforms with an average accuracy of 82.67% compared with the logistic regression, random forest, multinomial naïve Bayes and long short-term memory models, which present 78%, 77%, 68.67% and 75% accuracy, respectively.
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Affiliation(s)
- Shweta Agrawal
- Institute of Advanced Computing, SAGE University, Indore 452010, India
| | - Sanjiv Kumar Jain
- Electrical Engineering Department, Medi-Caps University, Indore 453331, India
| | - Shruti Sharma
- Department of Computer Science and Engineering, Indore Institute of Science &Technology, Indore 453332, India
| | - Ajay Khatri
- Bellurbis Technologies Private Limited, Indore 452001, India
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7
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Eysenbach G, Nowakowski S, Agrawal R, Sharafkhaneh A, Kunik ME, Naik AD, Xu H, Razjouyan J. Sentiment Analysis of Insomnia-Related Tweets via a Combination of Transformers Using Dempster-Shafer Theory: Pre- and Peri-COVID-19 Pandemic Retrospective Study. J Med Internet Res 2022; 24:e41517. [PMID: 36417585 PMCID: PMC9822178 DOI: 10.2196/41517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 10/20/2022] [Accepted: 11/15/2022] [Indexed: 11/24/2022] Open
Abstract
BACKGROUND The COVID-19 pandemic has imposed additional stress on population health that may result in a change of sleeping behavior. OBJECTIVE In this study, we hypothesized that using natural language processing to explore social media would help with assessing the mental health conditions of people experiencing insomnia after the outbreak of COVID-19. METHODS We designed a retrospective study that used public social media content from Twitter. We categorized insomnia-related tweets based on time, using the following two intervals: the prepandemic (January 1, 2019, to January 1, 2020) and peripandemic (January 1, 2020, to January 1, 2021) intervals. We performed a sentiment analysis by using pretrained transformers in conjunction with Dempster-Shafer theory (DST) to classify the polarity of emotions as positive, negative, and neutral. We validated the proposed pipeline on 300 annotated tweets. Additionally, we performed a temporal analysis to examine the effect of time on Twitter users' insomnia experiences, using logistic regression. RESULTS We extracted 305,321 tweets containing the word insomnia (prepandemic tweets: n=139,561; peripandemic tweets: n=165,760). The best combination of pretrained transformers (combined via DST) yielded 84% accuracy. By using this pipeline, we found that the odds of posting negative tweets (odds ratio [OR] 1.39, 95% CI 1.37-1.41; P<.001) were higher in the peripandemic interval compared to those in the prepandemic interval. The likelihood of posting negative tweets after midnight was 21% higher than that before midnight (OR 1.21, 95% CI 1.19-1.23; P<.001). In the prepandemic interval, while the odds of posting negative tweets were 2% higher after midnight compared to those before midnight (OR 1.02, 95% CI 1.00-1.07; P=.008), they were 43% higher (OR 1.43, 95% CI 1.40-1.46; P<.001) in the peripandemic interval. CONCLUSIONS The proposed novel sentiment analysis pipeline, which combines pretrained transformers via DST, is capable of classifying the emotions and sentiments of insomnia-related tweets. Twitter users shared more negative tweets about insomnia in the peripandemic interval than in the prepandemic interval. Future studies using a natural language processing framework could assess tweets about other types of psychological distress, habit changes, weight gain resulting from inactivity, and the effect of viral infection on sleep.
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Affiliation(s)
| | - Sara Nowakowski
- Department of Medicine, Baylor College of Medicine, Houston, TX, United States
| | - Ritwick Agrawal
- Department of Medicine, Baylor College of Medicine, Houston, TX, United States
| | - Amir Sharafkhaneh
- Department of Medicine, Baylor College of Medicine, Houston, TX, United States
| | - Mark E Kunik
- Department of Medicine, Baylor College of Medicine, Houston, TX, United States
| | - Aanand D Naik
- Department of Management, Policy, and Community Health, University of Texas School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Hua Xu
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Javad Razjouyan
- Department of Medicine, Baylor College of Medicine, Houston, TX, United States
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8
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Botz J, Wang D, Lambert N, Wagner N, Génin M, Thommes E, Madan S, Coudeville L, Fröhlich H. Modeling approaches for early warning and monitoring of pandemic situations as well as decision support. Front Public Health 2022; 10:994949. [PMID: 36452960 PMCID: PMC9702983 DOI: 10.3389/fpubh.2022.994949] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Accepted: 10/21/2022] [Indexed: 11/15/2022] Open
Abstract
The COVID-19 pandemic has highlighted the lack of preparedness of many healthcare systems against pandemic situations. In response, many population-level computational modeling approaches have been proposed for predicting outbreaks, spatiotemporally forecasting disease spread, and assessing as well as predicting the effectiveness of (non-) pharmaceutical interventions. However, in several countries, these modeling efforts have only limited impact on governmental decision-making so far. In light of this situation, the review aims to provide a critical review of existing modeling approaches and to discuss the potential for future developments.
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Affiliation(s)
- Jonas Botz
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
| | - Danqi Wang
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, Bonn, Germany
| | | | | | | | | | - Sumit Madan
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
- Department of Computer Science, University of Bonn, Bonn, Germany
| | | | - Holger Fröhlich
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, Bonn, Germany
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Modi A, Shah K, Shah S, Patel S, Shah M. Sentiment Analysis of Twitter Feeds Using Flask Environment: A Superior Application of Data Analysis. ANNALS OF DATA SCIENCE 2022; 11:1-22. [PMID: 38625244 PMCID: PMC9554374 DOI: 10.1007/s40745-022-00445-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 07/25/2022] [Accepted: 08/31/2022] [Indexed: 11/07/2022]
Abstract
In this challenging world, social media plays a vital role as it is at the pinnacle of data sharing. The advancement in technology has made a huge amount of information available for data analysis and it is on the hotlist nowadays. Opinions of the people are expressed and shared across various social media platforms like Twitter, Facebook, and Instagram. Twitter is a prodigious platform containing an ample amount of data and analyzing the data is of topmost priority. One of the most widely utilized approaches for classifying an individual's emotions displayed in subjective data is sentiment analysis. Sentiment analysis is done using various algorithms of machine learning like Support Vector Machine, Naive Bayes, Long Short-Term Memory, Decision Tree Classifier, and many more, but this paper aims at the generalized way of performing Twitter sentiment analysis using flask environment. Flask environment provides various inbuilt functionalities to analyze the sentiments of text into three different categories: positive, negative, and neutral. Also, it makes API calls to the Twitter Developer account to fetch the Twitter data. After fetching and analyzing the data, the results get displayed on a webpage containing the percentage of positive, negative, and neutral tweets for a phrase in a pie chart. It displays the language analysis for the same phrase. Furthermore, the webpage calls attention to the tweets done on that phrase and reveals the details of the tweets. Considering the major industry runners of three different sectors namely Enterprises, Sports Apparel Industry, and Multimedia Industry, we have analyzed and compared sentiments of two different Multinational companies from each sector.
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Affiliation(s)
- Astha Modi
- Department of Information and Communication Technology, School of Technology, Pandit Deendayal Energy University, Gandhinagar, Gujarat 382426 India
| | - Khelan Shah
- Department of Information and Communication Technology, School of Technology, Pandit Deendayal Energy University, Gandhinagar, Gujarat 382426 India
| | - Shrey Shah
- Department of Information and Communication Technology, School of Technology, Pandit Deendayal Energy University, Gandhinagar, Gujarat 382426 India
| | - Samir Patel
- Department of Computer Science, School of Technology, Pandit Deendayal Energy University, Gandhinagar, Gujarat 382426 India
| | - Manan Shah
- Department of Chemical Engineering, School of Energy Technology, Pandit Deendayal Energy University, Gandhinagar, Gujarat 382426 India
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Ethical Considerations in the Application of Artificial Intelligence to Monitor Social Media for COVID-19 Data. Minds Mach (Dordr) 2022; 32:759-768. [PMID: 36042870 PMCID: PMC9406274 DOI: 10.1007/s11023-022-09610-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Accepted: 08/04/2022] [Indexed: 10/27/2022]
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11
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A Novel Benchmark Dataset for COVID-19 Detection during Third Wave in Pakistan. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:6354579. [PMID: 35990145 PMCID: PMC9391128 DOI: 10.1155/2022/6354579] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Revised: 07/04/2022] [Accepted: 07/13/2022] [Indexed: 11/17/2022]
Abstract
Coronavirus (COVID-19) is a highly severe infection caused by the severe acute respiratory coronavirus 2 (SARS-CoV-2). The polymerase chain reaction (PCR) test is essential to confirm the COVID-19 infection, but it has certain limitations, including paucity of reagents, is computationally time-consuming, and requires expert clinicians. Clinicians suggest that the PCR test is not a reliable automated COVID-19 patient detection system. This study proposed a machine learning-based approach to evaluate the PCR role in COVID-19 detection. We collect real data containing 603 COVID-19 samples from the Pakistan Institute of Medical Sciences (PIMS) Hospital in Islamabad, Pakistan, during the third COVID-19 wave. The experiments are separated into two sets. The first set comprises 24 features, including PCR test results, whereas the second comprises 24 features without PCR test. The findings demonstrate that the decision tree achieves the best detection rate for positive and negative COVID-19 patients in both scenarios. The findings reveal that PCR does not contribute to detecting COVID-19 patients. The findings also aid in the early detection of COVID-19, mainly when PCR test results are insufficient for diagnosing COVID-19 and help developing countries with a paucity of PCR tests and specialist facilities.
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12
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A Novel Cascade Model for End-to-End Aspect-Based Social Comment Sentiment Analysis. ELECTRONICS 2022. [DOI: 10.3390/electronics11121810] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
The end-to-end aspect-based social comment sentiment analysis (E2E-ABSA) task aims to discover human’s fine-grained sentimental polarity, which can be refined to determine the attitude in response to an object revealed in a social user’s textual description. The E2E-ABSA problem includes two sub-tasks, i.e., opinion target extraction and target sentiment identification. However, most previous methods always tend to model these two tasks independently, which inevitably hinders the overall practical performance. This paper investigates the critical collaborative signals between these two sub-tasks and thus proposes a novel cascade social comment sentiment analysis model for jointly tackling the E2E-ABSA problem, namely CasNSA. Instead of treating the opinion target extraction and target sentiment identification as discrete procedures in previous works, our new framework takes the contextualized target semantic encoding into consideration to yield better sentimental polarity judgment. Additionally, extensive empirical results show that the proposed approach effectively achieves a 68.13% F1-score on SemEval-2014, 62.34% F1-Score on SemEval-2015, 56.40% F1-Score on SemEval-2016, and 50.05% F1-score on a Twitter dataset, which is higher than the existing approaches. Ablated experiments demonstrate that the CasNSA model substantially outperforms state-of-the-art methods, even when using fixed words embedding rather than pre-trained BERT fine tuning. Moreover, in-depth performance analysis on the social comment datasets further validates that our work gains superior performance and reliability effectively and efficiently in realistic scenarios.
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13
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Shakeel T, Habib S, Boulila W, Koubaa A, Javed AR, Rizwan M, Gadekallu TR, Sufiyan M. A survey on COVID-19 impact in the healthcare domain: worldwide market implementation, applications, security and privacy issues, challenges and future prospects. COMPLEX INTELL SYST 2022; 9:1027-1058. [PMID: 35668731 PMCID: PMC9151356 DOI: 10.1007/s40747-022-00767-w] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 04/15/2022] [Indexed: 12/23/2022]
Abstract
Extensive research has been conducted on healthcare technology and service advancements during the last decade. The Internet of Medical Things (IoMT) has demonstrated the ability to connect various medical apparatus, sensors, and healthcare specialists to ensure the best medical treatment in a distant location. Patient safety has improved, healthcare prices have decreased dramatically, healthcare services have become more approachable, and the operational efficiency of the healthcare industry has increased. This research paper offers a recent review of current and future healthcare applications, security, market trends, and IoMT-based technology implementation. This research paper analyses the advancement of IoMT implementation in addressing various healthcare concerns from the perspectives of enabling technologies, healthcare applications, and services. The potential obstacles and issues of the IoMT system are also discussed. Finally, the survey includes a comprehensive overview of different disciplines of IoMT to empower future researchers who are eager to work on and make advances in the field to obtain a better understanding of the domain.
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Affiliation(s)
- Tanzeela Shakeel
- School of System and Technology, University of Management and Technology, Lahore, Pakistan
| | - Shaista Habib
- School of System and Technology, University of Management and Technology, Lahore, Pakistan
| | - Wadii Boulila
- Robotics and Internet of Things Lab, Prince Sultan University, Riyadh, 12435 Saudi Arabia
| | - Anis Koubaa
- Robotics and Internet of Things Lab, Prince Sultan University, Riyadh, 12435 Saudi Arabia
| | - Abdul Rehman Javed
- Department of Cyber Security, PAF Complex, E-9, Air University, Islamabad, Pakistan
| | - Muhammad Rizwan
- Department of Computer Science, Kinnaird College for Women, Lahore, Pakistan
| | - Thippa Reddy Gadekallu
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India
| | - Mahmood Sufiyan
- School of System and Technology, University of Management and Technology, Lahore, Pakistan
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14
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Clustering based sentiment analysis on Twitter data for COVID-19 vaccines in India. Int J Health Sci (Qassim) 2022. [DOI: 10.53730/ijhs.v6ns2.6126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Coronavirus is a new and rapidly spreading viral disease. It is essential to have a vaccine in order to reduce the virus's impact. Vaccination-related sentiments can influence an individual's decision to accept the vaccines. Evaluating the sentiments is a time-consuming and challenging process. Sentiment analysis (SA) could have an impact on the vaccination initiatives as well as changes in people's opinions and behaviour around immunizations. Since social media is widely utilized to disseminate information, mining this data is a popular area of study these days. On Twitter, a wide range of opinions about the negative effects of licensed vaccines have been expressed over time. In this research, tweets are gathered, pre-processed to remove extraneous data, and then utilized for sentiments analysis utilizing the Lexicons-based technique and machine learning. After feature extraction, the clustering is performed using MEEM approach. This research proposed a Clustering Based Twitter sentiments analysis of COVID 19 (C-SAT COVID 19) vaccinations in India. An enhanced random forest classifier is implemented in this research to classify the sentiment scores provided by the sentiment analysis. A classification is performed based on the negative, neutral, and positive sentiment analysis to examine people's emotions towards vaccinations accessible in India.
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