This document describes the development of a sentiment analysis engine for classifying texts as positive or negative sentiment. It involves several steps: data preparation through cleaning, tagging parts of speech, and vectorization; training classification models including logistic regression, random forest, and extra trees; and updating the database with sentiment labels. Evaluation shows the classification engine achieves higher accuracy than the existing lexicon-based approach, particularly for positive texts, though accuracy drops slightly on some dates with an imbalance of negative texts. Overall, the classification approach improved the sentiment analysis accuracy for the target use case.