Exploring the Impact of Covid-19 on Mental Health in Singapore using NLP

Local Chapter Singapore Chapter

Coordinated bySingapore ,

Status: Completed

Project Duration: 01 Jul 2021 - 01 Sep 2021

Open Source resources available from this project

Project background.

The covid-19 pandemic has transformed the day-to-day lives of Singaporeans, and this has taken a toll on the nation’s mental health. A Straits Times survey of 1000 people in March 2021 found that 1 in 3 people felt his/her mental well-being had worsened since the circuit breaker in 2020. Social Organisations like the Singapore Muslim Women’s Association saw an increase in clients seeking mental health support, while helpline calls have increased at the Suicide prevention agency Samaritans of Singapore and the Singapore Association of Mental Health by 24% and 50% respectively.

Read more:
– https://www.straitstimes.com/singapore/covid-19-pandemic-brought-womens-mental-health-needs-to-the-forefront-muslim-womens

– https://www.channelnewsasia.com/singapore/singapore-mental-health-awareness-stigma-conditions-depression-1973166

– https://www.straitstimes.com/singapore/covid-19-pandemic-brought-womens-mental-health-needs-to-the-forefront-muslim-womens

The problem.

This project aims to uncover the impact of Covid-19 on mental health in Singapore, by collecting and analysing public sentiments in the ongoing Covid-19 outbreak. Insights and findings will be shared with mental health NGOs in Singapore, who can use the findings to make more informed decisions on designing future campaigns. The dashboards built will be available publicly for public education.

Project goals.

- An interactive public dashboard (e.g. Tableau) on the project’s insights and findings.
- Detailed documentation on the methodologies of data collection, analysis, and models built

Project plan.

    Learning outcomes.

    1. Data scraping from online sources (e.g. social media, online forums, news sites)

    2. Sentiment and empath analysis on text data

    3. Data visualization and building dashboard

    4. Building a predictive model with text data

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