ABSTRACT
As the COVID-19 outbreak continues to change crucial aspects of daily life, many suspect that the virus has also had a considerable impact on mental health. This study uses natural language processing (NLP) and machine learning on comments from the website Reddit to determine the effects of the COVID-19 pandemic on 5 mental health communities: r/anxiety, r/depression, r/SuicideWatch, r/mentalhealth, and r/COVID19_support. By applying a support vector machine, we extracted features from the data to determine the issues that these subreddits were struggling with the most during the COVID-19 pandemic. We then used a long short-term memory (LSTM) recurrent neural network to study the change in sentiment of each subreddit over the course of the pandemic. Results indicated that, out of the potential factors studied, feelings of isolation had the most impact on mental health during COVID-19. Additionally, the average sentiment of users from r/COVID19_support has an inverse relationship with the number of new COVID-19 cases per month in the United States. Through this research, we revealed the effectiveness of support vector machines and LSTM neural networks in analyzing mental health in social media comments related to COVID-19. As the COVID-19 pandemic progresses and more data becomes available, processes like the one presented in this research can provide insight into the mental health communities that are most influenced by COVID-19 and the effects of the pandemic that cause the most mental health issues. These findings may produce valuable information for policymakers and mental health physicians. © 2022 IEEE.
ABSTRACT
As the COVID-19 outbreak continues to change crucial aspects of daily life, many suspect that the virus has also had a considerable impact on mental health. This study uses natural language processing (NLP) and machine learning on comments from the website Reddit to determine the effects of the COVID-19 pandemic on 5 mental health communities: r/anxiety, r/depression, r/SuicideWatch, r/mentalhealth, and r/COVID19_support. By applying a support vector machine, we extracted features from the data to determine the issues that these subreddits were struggling with the most during the COVID-19 pandemic. We then used a long short-term memory (LSTM) recurrent neural network to study the change in sentiment of each subreddit over the course of the pandemic. Results indicated that, out of the potential factors studied, feelings of isolation had the most impact on mental health during COVID-19. Additionally, the average sentiment of users from r/COVID19_support has an inverse relationship with the number of new COVID-19 cases per month in the United States. Through this research, we revealed the effectiveness of support vector machines and LSTM neural networks in analyzing mental health in social media comments related to COVID-19. As the COVID-19 pandemic progresses and more data becomes available, processes like the one presented in this research can provide insight into the mental health communities that are most influenced by COVID-19 and the effects of the pandemic that cause the most mental health issues. These findings may produce valuable information for policymakers and mental health physicians. © 2022 IEEE.
ABSTRACT
Accurate measures of productivity growth are an important policy tool but are difficult to obtain in South African circumstances. In this article, we review work on the measurement of farm-level productivity in South Africa since the earliest attempts at multifactor indices in the early 1990s. The focus is on total factor productivity, but single input measures such as labor and land productivity (yields) are also discussed. Measurements using time-series and cross-sectional data are discussed separately, along with measures to explain the effect of climate change. Data deficiencies are also pointed out. The article concludes that international collaboration should be maintained if important issues such as the COVID-19 impact, food security, climate change, and labor market shocks are to be successfully addressed.