Few things. Don't make a model from scratch, use a pre-trained one. There are plenty on hugging face. Another thing, later on, if you have your own data, you can use it to fine tune those models to better suit your task. This is a general approach to ML applications where data isn't available or not enough. Side note, speaking from experience, those large sentiment models that are out there do have great performance but some of them have been trained with large sentiment datasets that have inconsistencies. For instance, once I had to validate manually the performance on my data and noticed that the pre-trained models predicted the following sentence as POSITIVE sentiment but to a human, this is not positive: "oh yay, I love cold food...". So be careful and setup some sanity checks. Don't fully assume the predictions are accurate.