Does not 'removing the stopwords' affect Natural Language Processing Results in a high degree? Most stopword lists contain contradicting prepositions (before-after, into-out of) and negativity words (not, no). Removing such words from the text almost always changes the meaning drastically.
The questions are:

*

*How reliable is removing stopwords?

*Does it somehow balance out when data is large (how large)?

*Does removing the stopwords really worth it considering the loss of true message?

*What are your suggestions?


First of all, it depends on the tasks you are solving. For simple classification tasks, knowing the presence of content words might be enough to get sufficient accuracy. In that case, the so-called stop words can make the model more prone to overfitting, filtering out redundant information can speed things up.
Stop words do not carry meaning on their own, but only in the context of a sentence. If you use a model (a linear classifier, decision tree/forest) that is in principle incapable of leveraging the context, keeping the stop words cannot actually help. If you use more complex models (LSTM, Transformers) that can grasp the grammatical meaning of the stopwords, it does not make sense to remove them.
