Sentiment Analysis of Social Media Response and Spatial Distribution Patterns on the COVID-19 Outbreak: The Case Study of Italy

Fernandez, Gabriela; Maione, Carol; Zaballa, Karenina; Bonnici, Norbert; Spitzberg, Brian H.; Carter, Jarai; Yang, Harrison Hao; McKew, Jack; Bonora, Filippo; Ghodke, Shraddha S.; Jin, Chanwoo; Ocampo, R.; Kepner, Wayne; Tsou, Ming‐Hsiang

doi:10.1007/978-3-030-83010-6_9

Cited by 6 publications

(4 citation statements)

References 27 publications

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“…The study in [14] focused on the influence of the pandemic in Italy, which was among the countries most affected by COVID-19. The resulting lockdown under the MCO caused social, environmental, and economic changes.…”

Section: Related Workmentioning

confidence: 99%

“…The regions were often affected differently because of the socioeconomic gaps between them. According to [14], the southern region and islands had the highest unemployment rates in Italy, whereas the economic status of the central and northern regions was higher. The average unemployment rates in the south, islands, north, and centre during COVID-19 were 16.8%, 16.2%, 5.7%, and 8%, respectively.…”

Section: Related Workmentioning

confidence: 99%

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LexDeep: Hybrid Lexicon and Deep Learning Sentiment Analysis Using Twitter for Unemployment-Related Discussions During COVID-19

Mohamed¹,

Zain²,

Shaiba³

et al. 2023

Computers, Materials &Amp; Continua

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The COVID-19 pandemic has spread globally, resulting in financial instability in many countries and reductions in the per capita gross domestic product. Sentiment analysis is a cost-effective method for acquiring sentiments based on household income loss, as expressed on social media. However, limited research has been conducted in this domain using the LexDeep approach. This study aimed to explore social trend analytics using LexDeep, which is a hybrid sentiment analysis technique, on Twitter to capture the risk of household income loss during the COVID-19 pandemic. First, tweet data were collected using Twint with relevant keywords before (9 March 2019 to 17 March 2020) and during (18 March 2020 to 21 August 2021) the pandemic. Subsequently, the tweets were annotated using VADER (lexiconbased) and fed into deep learning classifiers, and experiments were conducted using several embeddings, namely simple embedding, Global Vectors, and Word2Vec, to classify the sentiments expressed in the tweets. The performance of each LexDeep model was evaluated and compared with that of a support vector machine (SVM). Finally, the unemployment rates before and during COVID-19 were analysed to gain insights into the differences in unemployment percentages through social media input and analysis. The results demonstrated that all LexDeep models with simple embedding outperformed the SVM. This confirmed the superiority of the proposed LexDeep model over a classical machine learning classifier in performing sentiment analysis tasks for domain-specific sentiments. In terms of the risk of income loss, the unemployment issue is highly politicised on both the regional and global scales; thus, if a country cannot combat this issue, the global economy will also be affected. Future research should develop a utility maximisation algorithm for household welfare evaluation, given the percentage risk of income loss owing to COVID-19.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

LexDeep: Hybrid Lexicon and Deep Learning Sentiment Analysis Using Twitter for Unemployment-Related Discussions During COVID-19

Mohamed¹,

Zain²,

Shaiba³

et al. 2023

Computers, Materials &Amp; Continua

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“…Good efforts have been made by a number of researchers to explore the impact of the COVID-19 pandemic on the economy and industrial sectors [1,9,11,12]. Fernandez, N. [13] investigated the economic consequences of the pandemic outbreak on the economies of 30 nations and found that the GDP is likely to be affected by 3-6% and the economies of some countries may contract by up to 13%. The study included the service-oriented economy, which was damaged and where jobs were at high risk.…”

Section: Introductionmentioning

confidence: 99%

The Status of the Saudi Construction Industry during the COVID-19 Pandemic

Almutairi,

Bakri,

AlMunifi

et al. 2023

Sustainability

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The outbreak of COVID-19 has had a profound impact on the Saudi construction industry as well as the country’s economy. The pandemic jeopardized the positive perspectives and growth in megaproject numbers, as it has amplified the constraints that prevent the construction sector from realizing growth. This research work is intended to evaluate the status of the Saudi construction industry during the COVID-19 pandemic. In order to achieve this objective, a three-phased methodology was developed. In the first part of the research, semi-structured interviews with forty industry experts were conducted. The gathered data from both, the literature review and interviews were synthesized. This process resulted in four domains to be explored: project performance measurement, workforce, supply chain, and financial management. The outcomes from phase one were then utilized to develop a questionnaire survey that was communicated to construction firms all over Saudi Arabia, for which 124 responses were received. Data analysis was carried out, and the obtained results were clarified and triangulated through a focus group discussion in the third phase of the research. The outcomes from the mixed-methods research methodology were aggregated to enrich and interpret findings and draw conclusions and recommendations. The findings indicate that the pandemic has had a total of ten core impacts. The highly impacted areas in the industry were the technical performance of projects, reduction in productivity, risk management practices, downsizing the scope of ongoing projects, reduction in new projects or contracts, material shortage, recruitment of manpower in the construction firms and affiliated projects, and the financial performance of the organization. In the meantime, it was found that the government implemented mitigation measures from which the sector benefited, where 27% of contractors obtained Saned system assistance, 51% received cash compensation and 22% received tax postponement. The scope of this study is limited to exploring the status of the Saudi construction industry (SCI) during the COVID-19 pandemic. The study findings are of added value and represent a significant contribution to the body of knowledge in the field. However, further research on the exit strategies bringing the industry to the new normal, including the use of cutting-edge technologies in the age of multi-faceted disruption would be of great importance.

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“…The COVID-19 outbreak rapidly caused widespread social and economic devastation. Efforts to contain it resulted in measures such as closing of borders and restrictions around travel, social activities, and religious events [1]. While lockdown strategies may be effective in reducing the spread of COVID-19 and maintaining general health system responsiveness, this single-disease response compromises care for a range of other health conditions.…”

Section: Introductionmentioning

confidence: 99%