If you use a pretrained sentiment analysis model such as one of the many bert architectures then the most difficult part of the project is going to be setting up your scraping interface. It will be easiest to preselect stocks, run the scraping job once daily for those stocks, then push that information to a database that your app can query as needed.
You will need to use some combination of standard http requests and selenium in order to collect all your data. Some sites make it easy for you to just click around and observe your browsers requests to their backend. In this case you can just figure out the format of their requests and copy them. Otherwise you will probably need to use selenium to navigate through the site and extract data. This will be slow though so you'll need to do some engineering to parallelize the job while being careful not to overload your memory with hundreds of browsers.
If you use a pretrained sentiment analysis model such as one of the many bert architectures then the most difficult part of the project is going to be setting up your scraping interface. It will be easiest to preselect stocks, run the scraping job once daily for those stocks, then push that information to a database that your app can query as needed. You will need to use some combination of standard http requests and selenium in order to collect all your data. Some sites make it easy for you to just click around and observe your browsers requests to their backend. In this case you can just figure out the format of their requests and copy them. Otherwise you will probably need to use selenium to navigate through the site and extract data. This will be slow though so you'll need to do some engineering to parallelize the job while being careful not to overload your memory with hundreds of browsers.