COVID-19 Public Sentiment Insights: A Text Mining Approach to the Gulf Countries

Albahli, Saleh; Algsham, A.; Aeraj, S.; Alsaeed, Mohammed; Alrashed, Mosab; Rauf, Hafiz Tayyab; Arif, Muchlis; Mohammed, Mazin Abed

doi:10.32604/cmc.2021.014265

Cited by 35 publications

(21 citation statements)

References 12 publications

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“…Evidence that this new strain may be passed from person to person has prompted the World Health Organization to elevate the epidemic to stage 5, which is a strong indication that an epidemic is imminent, and the use of association, correspondence and agreed moderate measures is shortlived.Therefore, this spread was increased to stage 6, which proves that the global epidemic continues. U.S. Department of Disease Control and Prevention(CDC) estimates that AI can help in the screening, tracking and predicting the current and future patients [5], [6]. Early detection and diagnosis are primary examples of AI usage in pandemics especially upon the availability of annotated datasets.…”

Section: B Recent Pandemics -Sars-cov Mers-cov and Swine Flumentioning

confidence: 99%

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The Recent Technologies to Curb the Second-Wave of COVID-19 Pandemic

et al. 2021

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Different epidemics, specially Coronavirus, have caused critical misfortunes in various fields like monetary deprivation, survival conditions, thus diminishing the overall individual fulfillment. Various worldwide associations and different hierarchies of government fraternity are endeavoring to offer the necessary assistance in eliminating the infection impacts but unfortunately standing up to the nonappearance of resources and expertise. In contrast to all other pandemics, Coronavirus has proven to exhibit numerous requirements such that curated appropriation and determination of innovations are required to deal with the vigorous undertakings, which include precaution, detection, and medication. Innovative advancements are essential for the subsequent pandemics where-in the forthcoming difficulties can indeed be approached to such a degree that it facilitates constructive solutions more comprehensively. In this study, futuristic and emerging innovations are analyzed, improving COVID-19 effects for the general public. Large data sets need to be advanced so that extensive models related to deep analysis can be used to combat Coronavirus infection, which can be done by applying Artificial intelligence techniques such as Natural Language Processing (NLP), Machine Learning (ML), and Computer vision to varying processing files. This article aims to furnish variation sets of innovations that can be utilized to eliminate COVID-19 and serve as a resource for the coming generations. At last, elaboration associated with future state-of-the-art technologies and the attainable sectors of AI methodologies has been mentioned concerning the post-COVID-19 world to enable the different ideas for dealing with the pandemic-based difficulties.

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Section: B Recent Pandemics -Sars-cov Mers-cov and Swine Flumentioning

confidence: 99%

“…5 5G + smart applications 5G technologies along with medical sensors and smart applications enable real-time data processing for remote health monitoring allowing access of information by medical staff. 6 Internet of things (IoT)…”

Section: Cloud Computingmentioning

confidence: 99%

The Recent Technologies to Curb the Second-Wave of COVID-19 Pandemic

et al. 2021

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“…Scientists all over the world are studying this pandemic to analyze the spreading dynamics, design effective control policies, predict the next possible outbreak centers, develop vaccines, and optimize vaccination strategies. COVID-19 virus samples, statistics of positive cases, existing policies, and environmental factors have become important data for COVID-19 related research [3][4][5][6][7]. Another example is spatiotemporal COVID-19 records, which most countries have gradually published through virus testing since early 2020.…”

Section: Introductionmentioning

confidence: 99%

COVID-Scraper: An Open-Source Toolset for Automatically Scraping and Processing Global Multi-Scale Spatiotemporal COVID-19 Records

et al. 2021

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In 2019, COVID-19 quickly spread across the world, infecting billions of people and disrupting the normal lives of citizens in every country. Governments, organizations, and research institutions all over the world are dedicating vast resources to research effective strategies to fight this rapidly propagating virus. With virus testing, most countries publish the number of confirmed cases, dead cases, recovered cases, and locations routinely through various channels and forms. This important data source has enabled researchers worldwide to perform different COVID-19 scientific studies, such as modeling this virus's spreading patterns, developing prevention strategies, and studying the impact of COVID-19 on other aspects of society. However, one major challenge is that there is no standardized, updated, and high-quality data product that covers COVID-19 cases data internationally. This is because different countries may publish their data in unique channels, formats, and time intervals, which hinders researchers from fetching necessary COVID-19 datasets effectively, especially for fine-scale studies. Although existing solutions such as John's Hopkins COVID-19 Dashboard and 1point3acres COVID-19 tracker are widely used, it is difficult for users to access their original dataset and customize those data to meet specific requirements in categories, data structure, and data source selection. To address this challenge, we developed a toolset using cloud-based web scraping to extract, refine, unify, and store COVID-19 cases data at multiple scales for all available countries around the world automatically. The toolset then publishes the data for public access in an effective manner, which could offer users a real time COVID-19 dynamic dataset with a global view. Two case studies are presented about how to utilize the datasets. This toolset can also be easily extended to fulfill other purposes with its open-source nature.

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“…People extensively used social media to convey their ideas, opinions, and feelings regarding precautious procedures forced by the government. Therefore, many research studies were conducted on utilizing Twitter to discuss the extent of societal acceptance of government procedures during the pandemic by utilizing the power of SA [46][47][48]. The authors in [46] built a model by using machine learning to predict people's responsiveness to government procedures, taken during the COVID-19 pandemic in five different areas of Saudi Arabia.…”

Section: Introductionmentioning

confidence: 99%

“…Other researchers [47] focused on estimating the feelings of people in Gulf countries, examining their actions regarding the pandemic by applying semantic analysis on Arabic language tweets. Three levels of semantic analysis have been listed in their study to measure citizens' feelings.…”

Section: Introductionmentioning

confidence: 99%

Semantic Partitioning and Machine Learning in Sentiment Analysis

Fayyoumi

Idwan

2021

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This paper investigates sentiment analysis in Arabic tweets that have the presence of Jordanian dialect. A new dataset was collected during the coronavirus disease (COVID-19) pandemic. We demonstrate two models: the Traditional Arabic Language (TAL) model and the Semantic Partitioning Arabic Language (SPAL) model to envisage the polarity of the collected tweets by invoking several, well-known classifiers. The extraction and allocation of numerous Arabic features, such as lexical features, writing style features, grammatical features, and emotional features, have been used to analyze and classify the collected tweets semantically. The partitioning concept was performed on the original dataset by utilizing the hidden semantic meaning between tweets in the SPAL model before invoking various classifiers. The experimentation reveals that the overall performance of the SPAL model competes over and better than the performance of the TAL model due to imposing the genuine idea of semantic partitioning on the collected dataset.

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COVID-19 Public Sentiment Insights: A Text Mining Approach to the Gulf Countries

Cited by 35 publications

References 12 publications

The Recent Technologies to Curb the Second-Wave of COVID-19 Pandemic

The Recent Technologies to Curb the Second-Wave of COVID-19 Pandemic

COVID-Scraper: An Open-Source Toolset for Automatically Scraping and Processing Global Multi-Scale Spatiotemporal COVID-19 Records

Semantic Partitioning and Machine Learning in Sentiment Analysis

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