the results depicted in figure - show us the class prediction probabilities and also the top features that contributed the maximum to the prediction decision making process these key features are also highlighted in the normalized movie review text our model performs quite well in this scenario and we can see the key features that contributed to the negative sentiment of this review including badsillydialogand shamewhich make sense besides thisthe word great contributed the maximum to the positive probability of and in fact if we had removed this word from our review textthe positive probability would have dropped significantly the following code runs similar analysis on test movie review with both actual and predicted sentiment of positive value in [ ]doc_index interpret_classification_model_prediction(doc_index=doc_indexcorpus=norm_test_ reviewscorpus=test_reviewsprediction_labels=test_ sentimentsexplainer_obj=explainertest document index actual sentimentpositive predicted sentiment['positive'reviewi really liked the movie "joe it has really become cult classic among certain age groups the producer of this movie is personal friend of mine he is my stepsons father-in-law he lives in manhattan' west sideand has bungalow in southamptonlong island his son-in-law live next door to his bungalow <br />presentlyhe does not do any producingbut dabbles in business with hbo movies <br />as personmr gil is real gentleman and wish he would have continued in the production business of move making model prediction probabilities('negative' ('positive' figure - model interpretation for our classification model' correct prediction for positive review