The goal of tone analysis is to identify tone from text. We focused on the following tones: alarmist, warning, reassuring, and explanatory. To detect tones from text automatically, we used a supervised machine learning approach. This is a classic text classification problem, and a usual practice in approaching such problems is to first examine text chunks using a Multinomial Naïve Bayes classifier (based on the bag-of-words model). The classifier is based on Bayes’s theorem with a feature model that is conditionally independent of the tone. The classifier is first trained using the features extracted from manually tagged text. After training, the classifier predicts tones for newly extracted, previously unseen, text.