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Abstract
At present the pandemic situation caused by Corona virus syndrome 2019 which is abbreviated as COVID-19, has made the lifespan of men at stake. Not only has it affected the economic condition and created health hazards of the peoples all over the world but also it tells upon their mental state. It is surprising but very difficult to assess the frequency. There are various reasons behind this mental disorder. They are recession from work, confined in house strategy, getting afraid of corona virus, and some more. In this paper, we focus on the use of Natural Language Processing (NLP) procedure to analyze tweets with regard to mental state. Training of significant prototypes has been provided to categorize all tweets into the emotions mentioned below: covid19, covid, COVID-19, covid 19, flu, virus, hantavirus, fever, cough, social distance, lockdown, pandemic, epidemic. We build the EmoCT (Emotion-Covid19-Tweet) dataset to train physically, tagging 1,500 English tweets. In addition to it, two procedures are suggested and distinguished to explore the causes which are creating melancholy and disquiet.
Highlights
For the sake of support, the community gets ready with regards to rising psychological trauma in the period of COVID-19 urgency
It is named Sequenced Treatment Alternatives to Relieve Depression (STAR*D): This has provided the statistics of depression revocation rates of more than 65% after the continuation of six months of therapy [17-21]
From the illustration and demonstration of the tweets related to depression and non-depression, we discover various features or illustrations concerning psycholinguistic calculations from the users’ texts
Summary
Psychological problem is a customary state now. A statistic is made by World Health Organization (WHO) [1], which says that out of four persons in the world, one will suffer from psychological or neurological hazards at any time of their lifespan. This research basically focuses on establishing the worth of social media such as Twitter for examining the psychological health perspective of the peoples It is performed by accumulating the Twitter public data that help to detect the negative perspective towards numerous psychological health hazards. Such outcomes are distinguished to the conventional sources of studies in such domain to establish the utility of the social media survey. The Twitter dataset that was implemented in this inspection procedure is heretofore classified into two groups such as positive and negative polarity of depression and in this way the security of the data turns out to be simple to monitor the impact of different attributes
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