 <p>TF-IDF as far as I understand is a feature. TF is term frequency i.e. frequency of occurence in a document. IDF is inverse document frequncy i.e frequency of documents in which the term occurs. </p> <p>Here, the model is using the TF-IDF info in the training corpus to estimate the new documents. For a very simple example, Say a document with word bad has pretty high term frequency of word bad in training set will sentiment label as negative. So, any new document containing bad will be more likely to be negative. </p> <p>For the accuracy you can manaually select training corpus which contains mostly used negative or positive words. This will boost the accuracy. </p>