Depression Analysis of Real Time Tweets During Covid Pandemic

The assessment of depression and suicidal tendencies among people due to covid-19 was less explored. The paper presents the real-time framework for the assessment of depression in covid pandemic. This approach gives a better alternate option to reduce the suicidal tendency in covid time with retweeting and other alternate real-time ways. Hence, the main objective of the present work is, to develop a real time frame-work to analyse sentiment and depression in people due to covid. The experimental investigation is carried out based on real time streamed tweets from twitter adopting lexicon and machine learning (ML) approach. Linear regression, K-nearest neighbor (KNN), Naive Bayes models are trained and tested with 1000 tweets to ascertain the accuracy for the sentiment’s distribution. Comparatively, the decision tree (98.75%) and Naive Bayes (80.33%) have shown better accuracy with the visualisation of data to draw any inferences from sentiments using word cloud. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

A Novel Deep Learning Based Healthcare Model for COVID-19 Pandemic Stress Analysis

Coronavirus (COVID-19) has impacted nearly every person across the globe either in terms of losses of life or as of lockdown. The current coronavirus (COVID-19) pandemic is a rare/special situation where people can express their feelings on Internet-based social networks. Social media is emerging as the biggest platform in recent years where people spend most of their time expressing themselves and their emotions. This research is based on gathering data from Twitter and analyzing the behavior of the people during the COVID-19 lockdown. The research is based on the logic expressed by people in this perspective and emotions for the suffering of COVID-19 and lockdown. In this research, we have used a Long Short-Term Memory (LSTM) network model with Convolutional Neural Network using Keras python deep-learning library to determine whether social media platform users are depressed in terms of positive, negative, or neutral emotional out bust based on their Twitter posts. The results showed that the model has 88.14% accuracy (representation of the correct prediction over the test dataset) after 10 epochs which most tweets showed had neutral polarity. The evaluation shows interesting results in positive (1), negative (-1), and neutral (0) emotions through different visualization. © 2022 Tech Science Press. All rights reserved.