TY - CPAPER
AU - Bano, Shahana
AU - Pranathi, Yerramreddy Lakshmi
AU - Niharika, Gorsa Lakshmi
AU - Sreya, Gorsa Datta Sai
PY - 2021
DA - March
TI - Prognostic of depression levels due to pandemic using LSTM
EP - 22
VL - 176
DO - https://doi.org/10.1007/978-981-33-4355-9_2
AB - Depression is a medical illness that affects the way you think and how you react. It is a serious medical issue that impacts the stability of the mind. Depression occurs at many stages and situations. With the help of classification, the stage of depression the person is in can be tried to categorize. Nowadays, many users are sharing their views on social media, and it became a platform for knowing people around us. From the data that is shared on social media, the depressing posts are being classified using machine learning techniques. With these reports collected, the depressed person might be helped from making any sudden decisions. So, in our research study, the large datasets of the people in depression during the COVID-19 pandemic situations is analyzed and not in pandemic situations. Here to analyze the data, the neural networks have been trained with the current pandemic analysis report, and it has given a prediction that the people are less likely to get depressed when they are not in a pandemic situation like COVID-19.
PB - Springer Nature
UR - https://rgu-repository.worktribe.com/output/2064034
KW - Coronavirus (COVID-19)
KW - Mental health and wellbeing
ER