Requirements NodeJS v11.4.0 or above.
$ npm install
$ npm run develop
- Bump version
npm version [major|minor|...] - Git push
git push && git push --tags - Deploy
npm run build-and-deployto ELECT's gitlab. - Ping P'Tai in Slack
| Name | URL |
|---|---|
| Staging | https://kind-bardeen-92630a.netlify.com |
| Date | Status | Links |
|---|---|---|
| 2019/07/25 | Completed |   |
| 2020/02/24-27 | Completed | |
- Use machine learning algorithms to automatically identify speakers in each part of meetings. From what I see, this scenario is probably a Blind Source Separation problem. Hence, we might start with Independent Component Analysis (ICA).
Apart from being a tool that provides an efficient way to listen to parliament meetings, its datasets are also valuable. These datasets could results in several applications including:
- Performance metric of each member of parliament (MP). Althought it's a rough estimation, having this metric would allow people to see how their representatives perform.
- Inspired by the famous online game Fantasy Football, we could build Fantasy Politics in which people will play by picking a squad of politicians before next meeting. How members in the squad participate in the meeting will result scores that the player will receive after the meeting.
- Inspired by music streaming services, i.e. Spotify or Fungjai, we could have a similar platform, Fungkai Dee, offering tracks of politicians talking in parliament meetings.
- This projects was bootstrapped with Gatsby.
The code is licensed under the MIT License
Data is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0)
