rtweet VS hts

This is probably what you're looking for: https://docs.ropensci.org/rtweet/

{rtweet}, which interacts fairly well with twitter's API (but you need to get a API key to use it, IIRC).

Use the rtweet package. I was able to follow the guide linked on that page fine an undergrad, and I don’t think I had to lie and say I was a grad student. And if I did they certainly didn’t demand any documentation.

https://github.com/ropensci/rtweet/issues/251#issuecomment-464338343

https://github.com/ropensci/rtweet or similar to access the data.

sadly a lot of statistics research is done with R and is unavailable with Python, hopefully this kind of work will also motivate new libraries for Python. I am particularly interested in hierarchical forecasting. Are there Python alternatives to the hts library?(https://github.com/earowang/hts)

Additionally, you could use one of the more complex methods from the aforementioned hts package. This will allow you to make forecasts on all levels of the hierarchy, and use the bootstrapped errors to make adjustments to all forecasts in the hierarchy using a constrained least-squares approach, in order to make all forecasts sum-consistent (make the aggregates of the forecasts equal the forecasts of the aggregates). This allows you to model cannibalisation effects between different products, for example. However for this to work, you'd need quite good models, as the bootstrapped errors are taken as the 'wiggle room' for the adjustments, which means that if you have a badly fitting model, the adjustments might be quite large and no longer make sense (eg. be negative for a sales forecast).