best practice to start a sentiment analysis project?
I think you might miss something here, so to train a sentiment analysis model, you will have a train data which every row has label (positive or negative) and a raw text. In order to make computer can understand or "see" the text is by representing the text as number (since computer cannot understand text), so one of the way to represent text as number is by using bag of words (there are other methods to represent text like TF/IDF, WORD2VEC, etc.). So when you train the model using data train, the program should preprocess the raw text, then it should make (in this case) a bag of words map where every element position represent one vocabulary, and it will become 1 or more if the word exist in the text and 0 if it doesn't exist.
Now suppose the training has finished, then the program produce a model, this model is what you save, so whenever you want to test a data, you don't need to re-train the program again. Now when you want to test, yes, you will use the bag of words mapping of the train data, suppose there is a word in the test dataset that never occurred in train dataset, then just map it as 0.
in short:
when you want to test, you have to use the bag of words mapping from the data train
After i did a lot of research about AI and sentiment analysis i found 2 ways to do text analysis.
After the pre-processing for text is done we must create a classification in order to get the positive and negative, so my question is it better to have example:
first way:

100 records of text to train that includes 2 fields text &
status filed that indicate if its positive 1 or negative 0.

second way:
100 records of text to train and make a vocabulary for bag of word in order to train and compare the tested records based on this bag of word.
if i am mistaking in my question please tel me and correct my question.