Air and water health: industrial footprints of COVID-19 imposed lockdown

Gupta, Vishal; Jain, Nikita; Virmani, Deepali; Ahmadian, Ali; Феррара, Массимилиано

doi:10.1007/s12517-022-09986-4

Cited by 4 publications

(2 citation statements)

References 43 publications

(18 reference statements)

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“…Bonnevie et al [27] measured the opposition to the COVID-19 vaccine with the Twitter dataset. Recent research performed prediction [28], environmental [29], health [30,31], socioeconomic [32], emotional [33] impact of COVID-19. Iwendi et al [34] suggested a new strategy to detect fake news related to COVID-19.…”

Section: Sentiment Analysismentioning

confidence: 99%

Sigmoidal Particle Swarm Optimization for Twitter Sentiment Analysis

Kumar¹,

Khan²,

Hasanat³

et al. 2023

Computers, Materials &Amp; Continua

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Social media, like Twitter, is a data repository, and people exchange views on global issues like the COVID-19 pandemic. Social media has been shown to influence the low acceptance of vaccines. This work aims to identify public sentiments concerning the COVID-19 vaccines and better understand the individual's sensitivities and feelings that lead to achievement. This work proposes a method to analyze the opinion of an individual's tweet about the COVID-19 vaccines. This paper introduces a sigmoidal particle swarm optimization (SPSO) algorithm. First, the performance of SPSO is measured on a set of 12 benchmark problems, and later it is deployed for selecting optimal text features and categorizing sentiment. The proposed method uses TextBlob and VADER for sentiment analysis, CountVectorizer, and term frequency-inverse document frequency (TF-IDF) vectorizer for feature extraction, followed by SPSO-based feature selection. The Covid-19 vaccination tweets dataset was created and used for training, validating, and testing. The proposed approach outperformed considered algorithms in terms of accuracy. Additionally, we augmented the newly created dataset to make it balanced to increase performance. A classical support vector machine (SVM) gives better accuracy for the augmented dataset without a feature selection algorithm. It shows that augmentation improves the overall accuracy of tweet analysis. After the augmentation performance of PSO and SPSO is improved by almost 7% and 5%, respectively, it is observed that simple SVM with 10-fold cross-validation significantly improved compared to the primary dataset.

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Section: Sentiment Analysismentioning

confidence: 99%

Sigmoidal Particle Swarm Optimization for Twitter Sentiment Analysis

Kumar¹,

Khan²,

Hasanat³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…The fractional fuzzy models created to analyze the COVID problems through the fractional techniques [25] , [26] , [27] , [28] , [29] . The researchers works on COVID-19 in various categories like detection [30] and prediction [31] of COVID-19 using deep learning, IoT technology for medical advice [32] and the interrelation between the lockdown and the air and water quality during the pandemic [33] . Therefore, by collaborating the fuzzy MCDM methods, this work proposes a integrated MCDM method, which could perform different tasks in the proposed algorithm.…”

Section: Introductionmentioning

confidence: 99%