I have created a data set with 30.000 text documents (each text file is rather small with respect to its length), which are labelled with 0 and 1. Using this data set, I want to train machine learning and deep learning models in order to be able to classify new text files.
On the one hand, I want to use classical machine learning models (such as logistic regression, random forest, SVM, etc.) with the Bag of Words/TF-IDF approach. This requires extensive text pre-processing, such as tokenization, stemming, converting to lower case, removing of stopwords and punctuation, lemmatization, etc.
On the other hand, I want to use new deep learning models (such as RNN, LSTM, BERT, XLNET, etc.).
Which pre-processing steps are necessary/advantageous for these deep learning models? Should I also use tokenization, stemming, converting to lower case, removing of stopwords and punctuation, lemmatization, etc. or can I omit most of these steps?