During the COVID-19 era, mental health problems become increasingly crucial [Bueno-Notivol et al., 2021] [Yamada 2021]. Our project aimed to provide an online platform for people to write diaries, share stories, and make friends.

What It Does

The users can write journals on our website, and our LSTM machine learning model [Hochreiter, 1997] will analyze the score of happiness based the language and tell the users, on a scale of 100, how happy they are in the day. Besides, users are able to view the history of happiness and share their story in the community.

How we built it

Our backend is written in SQL and Python. Our front end is written in HTML, CSS, and Javascript.

Challenges We Ran into

All of us are amateurs to web development (especially Javascript and data fetching), so we had to learn both front end and back end from scratch and then build a working website. The hardest part is the synchronization between the two parts.

Accomplishments that we're proud of

We are very proud of the progress we make in web building. And we figure out how to connect the frontend and the backend. And our projects can actually help a lot of people feel better and become happier in a healthy community. Our model also has a very high accuracy which is around 85%.

What We Learned

All of us know nothing about JavaScript and SQL before we attended this Unihack'21 event. During the hackathon, we taught everything by ourselves and learned a lot about front end and back end as well as how to connect front end and back end.

What's Next for Happy Community

We are planing to continue building our project even though the event is over. We plan to add new features such as viewing journal histories and posting stories in the community. We want to plan to add visualizations to demonstrate the score to help one track their happiness score over time.


  • Bueno-Notivol et al. (2021). "Prevalence of depression during the COVID-19 outbreak: A meta-analysis of community-based studies". Int J Clin Health Psychol. 21(1): 100196. doi: 10.1016/j.ijchp.2020.07.007.
  • Hochreiter, Sepp and Schimidhuber Jürgen (1997). "Long Short-Term Memory". Natural Computation 9(8): 1735-1780. doi: 10.1162/neco.1997.9.8.1735.
  • Yuku Yamada et al. (2021). "COVIDiSTRESS Global Survey dataset on psychological and behavioural consequences of the COVID-19 outbreak". Scientific 8(3). doi: 10.1038/s41597-020-00784-9.
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