remove this formatting before we proceedin[ ]text_train [doc replace( "" "for doc in text_trainthe type of the entries of text_train will depend on your python version in python they will be of type bytes which represents binary encoding of the string data in python text_train contains strings we won' go into the details of the different string types in python herebut we recommend that you read the python and/or python documentation regarding strings and unicode the dataset was collected such that the positive class and the negative class balancedso that there are as many positive as negative stringsin[ ]print("samples per class (training){}format(np bincount(y_train))out[ ]samples per class (training)[ we load the test dataset in the same mannerin[ ]reviews_test load_files("data/aclimdb/test/"text_testy_test reviews_test datareviews_test target print("number of documents in test data{}format(len(text_test))print("samples per class (test){}format(np bincount(y_test))text_test [doc replace( "" "for doc in text_testout[ ]number of documents in test data samples per class (test)[ the task we want to solve is as followsgiven reviewwe want to assign the label "positiveor "negativebased on the text content of the review this is standard binary classification task howeverthe text data is not in format that machine learning model can handle we need to convert the string representation of the text into numeric representation that we can apply our machine learning algorithms to representing text data as bag of words one of the most simple but effective and commonly used ways to represent text for machine learning is using the bag-of-words representation when using this representationwe discard most of the structure of the input textlike paragraphssentencesand formattingand only count how often each word appears in each text in representing text data as bag of words