Doing Sentiment Analysis using NLP Libraries Conclusion from the Frist Approach : Sentiment Analysis using NLP Libraries (Unsupervised learning ) : result and analysis: 1) AFINN lexicon Model Performance metrics: ------------------------------ Accuracy: 0.71 Precision: 0.73 Recall: 0.71 F1 Score: 0.71 The Accuracy is 71% and F1 score tell about the performance of the that is 72%.that getting success is 72%. 2) SentiWordnet Model Performance metrics: ------------------------------ Accuracy: 0.69 Precision: 0.69 Recall: 0.69 F1 Score: 0.68 The Accuracy is 71% and F1 score tell about the performance of the that is 72%.that getting success is 72%. 3) VADER Model Performance metrics: ------------------------------ Accuracy: 0.71 Precision: 0.72 Recall: 0.71 F1 Score: 0.71 The Accuracy is 71% and F1 score tell about the performance of the that is 72%.that getting success is 72%. From comparing all three unsupervised model the AFFIN is best model because the precise value is greater than...