This tutorial uses Python's Scikit-Learn library to build a simple sentiment analysis model that can classify tweets as positive or negative. The dataset used is pre-labeled and consists of Twitter tweets already categorized as positive or negative. The model uses the Logistic Regression algorithm, which is a linear model commonly used for binary classification tasks. The dataset is formatted using the CountVectorizer class from Scikit-Learn to convert the tweets into a matrix of word counts, which are then used to train the classifier. The model is trained on 80% of the data and evaluated on the remaining 20%. The accuracy of the model is calculated using the accuracy_score function from Scikit-Learn, resulting in an accuracy of around 80%. The tutorial highlights the importance of considering factors such as data quality, parameter tuning, and additional training data to improve the performance of machine learning models.