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- W4301431353 abstract "The coronavirus pandemic hit the worldwide population to a large extent. But, one of the subtle effects of the COVID-19 pandemic was the depletion of the mental health of the people. Social media has become an efficient platform to express oneself, and Twitter is one of the most used platforms. There has been work where machine and deep learning were employed for tweet sentimental analysis for different applications including mental depression. Most of tweets sentimental analysis were focussed on positive and negative. There has been some research where neutral tweets were taken into consideration. In this research work, we have focussed on predicting depression of people, i.e. depressed, non-depressed and neutral from tweets during lock down period by employing deep learning models like bidirection long short-term memory (Bi-LSTM), bi-directional encoder representations from transformers (BERT), and XLNET. Also, the BERT model has been modified by adding classification layer for tweet classification. In addition, the exploratory data analysis was performed for postlockdown tweets." @default.
- W4301431353 created "2022-10-05" @default.
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- W4301431353 date "2022-10-06" @default.
- W4301431353 modified "2023-09-27" @default.
- W4301431353 title "Application of Deep Learning for COVID Twitter Sentimental Analysis Towards Mental Depression" @default.
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- W4301431353 doi "https://doi.org/10.1007/978-981-19-3575-6_14" @default.
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