BrainScribe

Inspiration

We were inspired by the challenges put forth to us by the recent outbreak. As the death tolls grew and the situation grew tense, we saw on a global scale the need to take a deep breath - a recess to escape reality. We discussed how we could help and in that call, birthed the idea of BrainScribe: a streamlined approach to therapy.

What it does

BrainScribe uses speech-to-text software as well as state-of-the-art sentiment-analysis to effectively track a user's sentiment every day - a statistic that it tracks and showcases to the user.

How we built it

We used ReactJS, S3, NodeJS, Django, the SKlearn Dataset, the PANDAS dataset, vectorizers, tokenizers, and many other tools regarding front- and back-end integration as well as NLP and speech-to-text.

Challenges we ran into

We ran into challenges involving speech-to-text and in the integration between speech-to-text and the sentiment-analysis.

Accomplishments that we're proud of

We're proud of being able to productively use the COVID-time to build an effective project. We're also happy we were able to learn so many things and bring them together in this project.

What we learned

We learned a lot about front- and back-end integration and were able to experiment with software like speech-to-text.

What's next for BrainScribe

We plan to expand beyond the hackathon to improve our sentiment analysis and make the graphs more accurate and more informative. We also hope to launch it publicly.

Built With

• artificial-intelligence
• css3
• html5
• machine-learning
• natural-language-processing
• node.js
• react
• sentiment-analysis
• topic-extraction