Sentiment analysis and its applications in fighting COVID-19 and infectious diseases: A systematic review

Alamoodi, A. H.; Zaidan, B. B.; Zaidan, A. A.; Albahri, O. S.; Mohammed, K. I.; Malik, Rami Qays; Almahdi, E. M.; Chyad, M. A.; Tareq, Ziadoon; Albahri, A. S.; Hameed, Hamsa; Alaa, Musaab

doi:10.1016/j.eswa.2020.114155

Cited by 287 publications

(196 citation statements)

References 63 publications

(146 reference statements)

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“…A recent survey retrieved a few studies that examined the presence of pandemics such as COVID-19 via sentiment analysis [22] , probably because the previous epidemics were smaller. Social media is an important news medium nowadays, studies that help understand people’s behavior could help authorities manage a situation.…”

Section: Related Workmentioning

confidence: 99%

Topic detection and sentiment analysis in Twitter content related to COVID-19 from Brazil and the USA

Garcia

Berton

2021

Applied Soft Computing

285

148

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Section: Related Workmentioning

confidence: 99%

Topic detection and sentiment analysis in Twitter content related to COVID-19 from Brazil and the USA

Garcia

Berton

2021

Applied Soft Computing

285

148

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“…A PRISMA framework is frequently used in the literature [16][17][18][19][20][21][22][23] which is followed as the review methodology of this study. e referenced review studies have considered the following issues in designing their review frameworks: (1) search strategy, (2) inclusion-exclusion criteria, (3) study selection, and (4) data extraction and classification.…”

Section: Review Methodologymentioning

confidence: 99%

Hospital Location Selection: A Systematic Literature Review on Methodologies and Applications

Gül

Güneri

2021

Mathematical Problems in Engineering

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The increased focus of people on the quality of health care in recent years has led hospital owners to develop strategies and policies to improve medical services through the establishment of new hospitals. For hospitals to be competitive, the hospitalʼs location and proximity to potential patients are considered crucial factors in establishing new hospitals. In this context, evaluating and selecting the most suitable hospital location to establish a new hospital from the multicriteria decision-making (MCDM) perspective is a priority for the entrepreneurs or government to gain a competitive advantage. Therefore, this study aims to present a systematic literature review of the hospital location selection problem considering the applied methods and application areas. The preferred reporting items for systematic review and meta-analysis statement (PRISMA) are used as a reference framework. Initially, known electronic databases (Web of science, IEEEXplore, Scopus, Science direct, and Google Scholar) were searched up to the early 2021. A number of 47 articles are selected and analyzed under this systematic framework based on inclusion-exclusion points. State-of-the-art developments in adopting MCDM methods and their fuzzy extensions are summarized. All the articles have been examined in a systematic taxonomy to find answers to six research questions (trend, country of origin, outlet journal, MCDM methods used, MCDM environment and criteria type, and decision criteria used). Results show that (1) AHP and GIS-based MCDM models are the most contributing approaches to the solution of this problem, (2) location selection criteria are mostly cost, demand, environment, population, government, competition in the market, and distance to some important places, (3) the fuzzy structure is also preferred in addition to the MCDM structure depending on the crisp data type, and (4) the location selection criteria are mostly considered subjective. We pay attention to promising directions that can dominate future research in this field from a methodological or applicability perspective. This study shows the current views and opportunities for researchers and practitioners and acts as a guide to encourage more creative studies in this field.

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“…Sentiment analysis is an extremely important tool to analyze and predict the key events and trends with ongoing time. In this context, Alamoodi et al have shown a great application in fighting against Covid-19 [45]. They aimed to develop a coherent database of all literature on similar medical and pandemic events from the past ten years that has been published with major online research paper databases.…”

Section: Recent Trends In Sentiment Analysismentioning

confidence: 99%

Predicting the pandemic: sentiment evaluation and predictive analysis from large-scale tweets on Covid-19 by deep convolutional neural network

Das

Kolya²

2021

Evol. Intel.

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Engaging deep neural networks for textual sentiment analysis is an extensively practiced domain of research. Textual sentiment classification harnesses the full computational potential of deep learning models. Typically, these research works are carried either with a popular open-source data corpus, or self-extracted short phrase texts from Twitter, Reddit, or web-scrapped text data from other resources. Rarely do we see a large amount of data on a current ongoing event is being collected and cultured further. Also, an even more complex task would be to model the data from a currently ongoing event, not only for scaling the sentiment accuracy but also for making a predictive analysis for the same. In this paper, we propose a novel approach for achieving sentiment evaluation accuracy by using a deep neural network on live-streamed tweets on Coronavirus and future case growth prediction. We develop a large tweet corpus exclusively based on the Coronavirus tweets. We split the data into train and test sets, alongside we perform polarity classification and trend analysis. The refined outcome from the trend analysis helps to train the data to provide an incremental learning curvature for our neural network, and we obtain an accuracy of 90.67%. Finally, we provide a statistical-based future prediction for Coronavirus cases growth. Not only our model outperforms several previous state-of-art experiments in overall sentiment accuracy comparison for similar tasks, but it also maintains a throughout performance stability among all the test cases when tested with several popular open-source text corpora.

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Sentiment analysis and its applications in fighting COVID-19 and infectious diseases: A systematic review

Cited by 287 publications

References 63 publications

Topic detection and sentiment analysis in Twitter content related to COVID-19 from Brazil and the USA

Topic detection and sentiment analysis in Twitter content related to COVID-19 from Brazil and the USA

Hospital Location Selection: A Systematic Literature Review on Methodologies and Applications

Predicting the pandemic: sentiment evaluation and predictive analysis from large-scale tweets on Covid-19 by deep convolutional neural network

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