Mood of India During Covid-19 - An Interactive Web Portal Based on Emotion Analysis of Twitter Data

Venigalla, Akhila Sri Manasa; Chimalakonda, Sridhar; Vagavolu, Dheeraj

doi:10.1145/3406865.3418567

Cited by 38 publications

(12 citation statements)

References 4 publications

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“…These findings in young adults in the initial phase of lockdown are noteworthy. Happiness has been reported by the majority and concurs with the twitter study 7 , and the major difference is that our study was done in the middle and late adolescent population. Despite the existence of fearful or worried cognitions, nearly 87.5%…”

Section: Discussionsupporting

confidence: 85%

Lockdown: A Focus on Emotions & Coping in Adolescents

Kannappan

Thamby²,

Ramesh³

et al. 2020

IJMHNS

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Objective: The COVID lockdown has been the most significant life-changing experience this year. The sudden implementation of measures such as social distancing, closure of institutions, and restriction of movement may have affected the psychological well-being of adolescents. Keeping this in mind the present exploratory pilot study is an attempt to study and determine the psychological impact on adolescents during the lockdown. Material and Method: Data was collected from a sample of 461 adolescent respondents aged 14 to 20 years through an online survey Statistical Analysis: Descriptive statistics and Spearman’s correlation in SPSS version 24 Results: The majority of the study population (85.7%) reported happiness or a relaxed state of mind, and the next common emotion was boredom. Amongst the 69% of participants also conveyed that stressful/negative thoughts, lack of direct contact with friends were the predominant stressor. Coping behaviors highlighted were spending time with family (93.6%), communicating with friends (96.1%), engaging in hobbies (88.7%), talking to relatives (79.6%), studying (72.7%), and exercising (70.9%). In contrast, the majority (73.3%) indulged in excessive sleep or increased usage of social media platforms or apps. Conclusion: This study is the only survey to document adolescents’ mental health in India during the Covid-19 lockdown. The study highlights that happiness and boredom reflected the young mind of these adolescents, and significantly only one third reported any physical or mental health impact with the lockdown.

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Section: Discussionsupporting

confidence: 85%

Lockdown: A Focus on Emotions & Coping in Adolescents

Kannappan

Thamby²,

Ramesh³

et al. 2020

IJMHNS

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“…Lower vigor and higher fatigue scores were also reported in April compared with May. A study from India conducted during the early stages of the COVID-19 pandemic provided insights into the mood of the population derived from the emotional content of more than 86,000 Twitter posts ( Venigalla et al, 2020 ). The emotional content of tweets varied according to specific trigger events, such as the introduction and extension of lockdown restrictions.…”

Section: Discussionmentioning

confidence: 99%

Mood Responses Associated With COVID-19 Restrictions

2020

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The COVID-19 pandemic resulted in more than half the world’s population being placed in lockdown to stem the spread of the virus. The severe restrictions imposed in many nations had the potential to significantly influence the physical and psychological well-being of those affected. The aim of the current study was to investigate mood responses during the period of restrictions from March to June, 2020. Mood responses of 1,062 participants (386 male, 676 female) were collected using the Brunel Mood Scale, hosted on the In The Mood website www.moodprofiling.com . The mean pattern of mood responses reflected an inverse iceberg profile, characterized by significantly elevated scores for tension, depression, anger, fatigue, and confusion, and below average scores for vigor; a profile associated with increased risk of mental health issues. Females reported more negative mood scores than males. Participants in the ≤25 age group reported the most negative profiles whereas those in the ≥56 age group reported the least negative profiles. Mood differences related to education status were also evident. Finally, mood scores fluctuated over time, with profiles being most negative during April and June. Overall, results confirmed significant mood disturbance during the period of COVID-19 restrictions, representing increased risk of psychopathology.

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“…Previous deep learning studies focused on emotions have used observations for their studies (Chen et al, 2020;Montemurro, 2020;Thakur & Jain, 2020), Twitter APIs, or Tweepy APIs (Dubey, 2020;Venigalla et al, 2020) (Arolfo et al, 2020). Our study used deep learning-based NLP techniques with RoBERTa (Liu et al 2019), a pre-trained language model developed by Facebook to analyze Twitter APIs.…”

Section: Discussion Of This Studymentioning

confidence: 99%

Applying and Understanding an Advanced, Novel Deep Learning Approach: A Covid 19, Text Based, Emotions Analysis Study

et al. 2021

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The pandemic COVID 19 has altered individuals' daily lives across the globe. It has led to preventive measures such as physical distancing to be imposed on individuals and led to terms such as 'lockdown,' 'emergency,' or curfew' to emerge in various countries. It has affected society, not only physically and financially, but in terms of emotional wellbeing as well. This distress in the human emotional quotient results from multiple factors such as financial implications, family member's behavior and support, country-specific lockdown protocols, media influence, or fear of the pandemic. For efficient pandemic management, there is a need to understand the emotional variations among individuals, as this will provide insights into public sentiment towards various government pandemic management policies. From our investigations, it was found that individuals have increasingly used different microblogging platforms such as Twitter to remain connected and express their feelings and concerns during the pandemic. However, research in the area of expressed emotional wellbeing during COVID 19 is still growing, which motivated this team to form the aim: To identify, explore and understand globally the emotions expressed during the earlier months of the pandemic COVID 19 by utilizing Deep Learning and Natural language Processing (NLP). For the data collection, over 2 million tweets during February-June 2020 were collected and analyzed using an advanced deep learning technique of Transfer Learning and Robustly Optimized BERT Pretraining Approach (RoBERTa). A Reddit-based standard Emotion Dataset by Crowdflower was utilized for transfer learning. Using RoBERTa and the collated Twitter dataset, a multi-class emotion classifier system was formed. With the implemented methodology, a tweet classification accuracy of 80.33% and an average MCC score of 0.78 was achieved, improving the existing AI-based emotion classification methods. This study explains the novel application of the Roberta model during the pandemic that provided insights into changing emotional wellbeing over time of various citizens worldwide. It also offers novelty for data mining and analytics during this challenging, pandemic era. These insights can be beneficial for formulating effective pandemic management strategies and devising a novel, predictive strategy for the emotional well-being of an entire country's citizens when facing future unexpected exogenous shocks.

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Mood of India During Covid-19 - An Interactive Web Portal Based on Emotion Analysis of Twitter Data

Cited by 38 publications

References 4 publications

Lockdown: A Focus on Emotions & Coping in Adolescents

Lockdown: A Focus on Emotions & Coping in Adolescents

Mood Responses Associated With COVID-19 Restrictions

Applying and Understanding an Advanced, Novel Deep Learning Approach: A Covid 19, Text Based, Emotions Analysis Study

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