TF-IDF vs TF for classification
So in the example above if many of the positive reviews have the word 'positive' in them then this word will automatically have modified (and in general lower) TF-IDF scores simply because the majority of the documents in the dataset are positive (600 documents).


You're thinking of the word positive as example because you are a human and you know what to select. The words which have the highest frequency in English are stop words: the, a, is... These are useless and would add a lot of noise in your model. You can remove them using a predefined list of stop words, but what about words which are not stop words but are frequent enough, for instance food, place, chef... This is where IDF helps.