Tracking Public Depression from Tweets on COVID-19 and Its Comparison with Pre-pandemic Time

Social media especially the Twitter platform has become a good data-source in Japan for tracking various social issues including depression and other mental health problems. It can overcome the under-representation and sampling bias of the survey data. In this study, we develop a machine learning approach to predict depression of Japanese people and compare their depression levels between pre-pandemic (2018) and pandemic (2020) times. We use three datasets in this study in which the first dataset is used for model development and its validation, while the rest two are used as test datasets for depression prediction. These two datasets represent timeseries tweets for the years 2018 (pre-pandemic) and 2020 (pandemic), respectively. After preprocessing the tweets, the Bag-of-words (BOW) feature is computed for each test dataset, which is later fed to the trained Logistic Regression (LOGR) model to classify tweets into "Depressive"and "Non-Depressive"categories. An analysis on the classified tweets shows a significant increase of depressive tweets in 2020, when compared with those in 2018. The covid related depressive tweets was found 50.37% of the total covid-related tweets and 8.6% of the total depressive tweets in the 2020 dataset, which indicates an increased impact of depression on the Japanese people due to COVID-19. Also, the peak depression occurs in June and August 2020 just after the first peak of the death progression timeseries in Japan, which indicates the consequences or shocks of exponential death-turmoil along with the increasing economic uncertainty and mobility restrictions. The timely application of our method to suitable textual datasets can minimize the calamity of future disasters like COVID-19 as well as it can help making suitable policy decisions for sustainable solutions against depression. © 2022 IEEE.

Exploration of Despair Eccentricities Based on Scale Metrics with Feature Sampling Using a Deep Learning Algorithm.

The majority of people in the modern biosphere struggle with depression as a result of the coronavirus pandemic's impact, which has adversely impacted mental health without warning. Even though the majority of individuals are still protected, it is crucial to check for post-corona virus symptoms if someone is feeling a little lethargic. In order to identify the post-coronavirus symptoms and attacks that are present in the human body, the recommended approach is included. When a harmful virus spreads inside a human body, the post-diagnosis symptoms are considerably more dangerous, and if they are not recognised at an early stage, the risks will be increased. Additionally, if the post-symptoms are severe and go untreated, it might harm one's mental health. In order to prevent someone from succumbing to depression, the technology of audio prediction is employed to recognise all the symptoms and potentially dangerous signs. Different choral characters are used to combine machine-learning algorithms to determine each person's mental state. Design considerations are made for a separate device that detects audio attribute outputs in order to evaluate the effectiveness of the suggested technique; compared to the previous method, the performance metric is substantially better by roughly 67%.

Psychological Behavior Prediction through Sentiment Analysis Technics: Transformers and ML Approach

In the era of the COVID-19 epidemic, governments have imposed nationwide lockdowns, which make a huge change to people's daily routines. This last impacts indirectly the well-being of people's mental health. And due to social media, many conversations about these phenomena occur online, especially those related to people's emotions. Which brought challenges and opportunities for sentiment analysis researchers. In this article, we are interested in extracting correlations between this epidemic and its psychological effects by analyzing users' tweets through common Deep Learning and Machine Learning approaches used for text classification. This last goal is a crucial step to fulfill the main objective of our research: Developing an intelligent system that provides recommendations such as positive support and early alert to help people in case of specific needs particularly challenging mental states © 2022 Maryame Naji, Najima Daoudi and Rachida Ajhoun

Depressive Symptoms Feature-Based Machine Learning Approach to Predicting Depression Using Smartphone.

With the impact of the COVID-19 pandemic, the number of patients suffering from depression is rising around the world. It is important to diagnose depression early so that it may be treated as soon as possible. The self-response questionnaire, which has been used to diagnose depression in hospitals, is impractical since it requires active patient engagement. Therefore, it is vital to have a system that predicts depression automatically and recommends treatment. In this paper, we propose a smartphone-based depression prediction system. In addition, we propose depressive features based on multimodal sensor data for predicting depressive mood. The multimodal depressive features were designed based on depression symptoms defined in the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). The proposed system comprises a "Mental Health Protector" application that collects data from smartphones and a big data-based cloud platform that processes large amounts of data. We recruited 106 mental patients and collected smartphone sensor data and self-reported questionnaires from their smartphones using the proposed system. Finally, we evaluated the performance of the proposed system's prediction of depression. As the test dataset, 27 out of 106 participants were selected randomly. The proposed system showed 76.92% on an f1-score for 16 patients with depression disease, and in particular, 15 patients, 93.75%, were successfully predicted. Unlike previous studies, the proposed method has high adaptability in that it uses only smartphones and has a distinction of evaluating prediction accuracy based on the diagnosis.