Mental Health Analysis of Students in Major Cities of India During COVID-19

Agarwal, Ashi; Agarwal, Basant; Harjule, Priyanka; Agarwal, Ajay

doi:10.1007/978-981-33-4236-1_4

Cited by 5 publications

(2 citation statements)

References 18 publications

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“…In Adamu et al ( 2021 ), researchers used the methods of KNN, RF, NB, SVM, DT and LR for sentimental analysis of palliatives distribution during COVID days using twitter dataset containing 9803 Tweets. Agarwal et al performed the Mental Health Analysis of students by NLP using 330,841 tweets (Agarwal et al, 2021 ) while (Das and Dutta, 2020 ) extracted 410,643 tweets and used Scatter plot, line chart, LDA for public sentiments analysis during the lockdown. In Flint et al ( 2021 ), the researcher understand adults’ thoughts and behaviors using k-means clustering algorithm.…”

Section: Related Workmentioning

confidence: 99%

Sentimental and spatial analysis of COVID-19 vaccines tweets

Umair

Masciari

2022

J Intell Inf Syst

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The world has to face health concerns due to huge spread of COVID. For this reason, the development of vaccine is the need of hour. The higher vaccine distribution, the higher the immunity against coronavirus. Therefore, there is a need to analyse the people’s sentiment for the vaccine campaign. Today, social media is the rich source of data where people share their opinions and experiences by their posts, comments or tweets. In this study, we have used the twitter data of vaccines of COVID and analysed them using methods of artificial intelligence and geo-spatial methods. We found the polarity of the tweets using the TextBlob() function and categorized them. Then, we designed the word clouds and classified the sentiments using the BERT model. We then performed the geo-coding and visualized the feature points over the world map. We found the correlation between the feature points geographically and then applied hotspot analysis and kernel density estimation to highlight the regions of positive, negative or neutral sentiments. We used precision, recall and F score to evaluate our model and compare our results with the state-of-the-art methods. The results showed that our model achieved 55% & 54% precision, 69% & 85% recall and 58% & 64% F score for positive class and negative class respectively. Thus, these sentimental and spatial analysis helps in world-wide pandemics by identify the people’s attitudes towards the vaccines.

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Section: Related Workmentioning

confidence: 99%

Sentimental and spatial analysis of COVID-19 vaccines tweets

Umair

Masciari

2022

J Intell Inf Syst

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“…In addition to that, the COVID-19 pandemic has motivated researchers in the area of sentiment classification to explore many issues. Several studies [2,31] were conducted about the mental health problems among individuals due to the pandemic outbreak, which has led to various psychological effects. Similarly, several studies [17,45,57] have investigated people's behavior, attitudes, and how they respond to crises by utilizing sentiment labels to analyze their opinions and sentiments during the pandemic.…”

Section: Related Workmentioning

confidence: 99%

Enhanced sentiment analysis regarding COVID-19 news from global channels

et al. 2022

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For a healthy society to exist, it is crucial for the media to focus on disease-related issues so that more people are widely aware of them and reduce health risks. Recently, deep neural networks have become a popular tool for textual sentiment analysis, which can provide valuable insights and real-time monitoring and analysis regarding health issues. In this paper, as part of an effort to develop an effective model that can elicit public sentiment on COVID-19 news, we propose a novel approach Cov-Att-BiLSTM for sentiment analysis of COVID-19 news headlines using deep neural networks. We integrate attention mechanisms, embedding techniques, and semantic level data labeling into the prediction process to enhance the accuracy. To evaluate the proposed approach, we compared it to several deep and machine learning classifiers using various metrics of categorization efficiency and prediction quality, and the experimental results demonstrate its superiority with 0.931 testing accuracy. Furthermore, 73,138 pandemic-related tweets posted on six global channels were analyzed by the proposed approach, which accurately reflects global coverage of COVID-19 news and vaccination.

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Understanding the intent behind sharing misinformation on social media

Agarwal

Harjule

et al. 2022

Journal of Experimental & Theoretical Artificial Intelligen

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Mental Health Analysis of Students in Major Cities of India During COVID-19

Cited by 5 publications

References 18 publications

Sentimental and spatial analysis of COVID-19 vaccines tweets

Sentimental and spatial analysis of COVID-19 vaccines tweets

Enhanced sentiment analysis regarding COVID-19 news from global channels

Understanding the intent behind sharing misinformation on social media

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