Lesley Cordero's tutorial provides a comprehensive guide to performing sentiment analysis on Twitter data using Python's natural language processing library, nltk. The process begins with setting up the Python environment, including downloading necessary datasets of pre-labeled positive and negative tweets. The tutorial emphasizes the use of Jupyter Notebook for an interactive coding experience and outlines the steps to format data for analysis by converting tweets into tokenized dictionaries. The core of the analysis involves building a sentiment classifier using the Naive Bayes algorithm, which predicts sentiment based on word frequencies. Despite achieving an accuracy rate of approximately 83%, the tutorial notes that the model's performance is hindered by the messy, unprocessed nature of social media data, highlighting the challenge of accurately classifying text due to factors like typos and grammatical inconsistencies. The author concludes by acknowledging the limitations of the classifier and invites readers to follow her on Twitter for more insights and data science content.