Classifiers and accuracy
The question mixes two different notions: models (or algorithms) and accuracy. Let me clarify them.

Model (or Algorithm) is a classification technique and 'Accuracy' is one of the ways to evaluate the performance of the models.

You can choose any models(Naive bayes, SVM or other deep learning techniques) to implement your classifier. They are independent of 'Accuracy' or 'F1' or any other measures by which you want to test the performance.

At first, you shall pre-process the text (remove stopwords, punctuation, etc.) , and pre-processing is a choice that says how the data should look like before getting into the model. They do influence the model's performance, but not to a great extent when done right. Usually, pre-processing is applied on both the train and test set.

Model performance: Once you implement you model, you may want to see how well it generalises (performamce on unseen data). So, you shall split the dataset into two halves: training and test set. (Usually most of the authors split into 3 portions: training set, validation set(to avoid over-fitting) and test set). You shall train the model with training set, and the test set is used to evaluate the performance of the model.

Model evaluation: Once the model is trained on training data, predict the labels on the test set. So, you have two set of labels on test set: 1: ground truth (the actual labels indicated by the test set) and 2: predicted labels (the labels predicted by the model).
Now, use the evaluation metric of your choice (Let's assume that you want to choose 'accuracy' as evaluation metric).
Accuracy can simply be calculates as: (#Number of correctly predicted samples / #Total number of samples) * 100 . Where #Number of correctly predicted samples is the count of samples for which the ground truth label and predicted labels are the same.