we get an overall -score and model accuracy of %which is quite similar to the afinn based model the afinn based model only wins out on the average precision by %otherwiseboth models have similar performance classifying sentiment with supervised learning another way to build model to understand the text content and predict the sentiment of the text based reviews is to use supervised machine learning to be more specificwe will be using classification models for solving this problem we have already covered the concepts relevant to supervised learning and classification in under the section "supervised learningwith regard to details on building and evaluating classification modelsyou can head over to and refresh your memory if needed we will be building an automated sentiment text classification system in subsequent sections the major steps to achieve this are mentioned as follows prepare train and test datasets (optionally validation dataset pre-process and normalize text documents feature engineering model training model prediction and evaluation these are the major steps for building our system optionally the last step would be to deploy the model in your server or on the cloud figure - shows detailed workflow for building standard text classification system with supervised learning (classificationmodels figure - blueprint for building an automated text classification system (sourcetext analytics with pythonapress