Politeness is key to building healthy communication. Brown and Levinson’s influential theory of politeness (BL’s theory) proposes that polite speech can be characterized by the presence of face-saving speech acts while impolite speech is characterized by the presence of face-threatening speech acts. Most existing politeness resources neither operationalize nor control politeness under BL’s theory; instead, they typically consist of short utterances with an intuitive account of politeness. In this paper, we introduce a long-text corpus that includes sentence-level face-act annotations and document-level politeness scores. Following BL’s theory, we annotate politeness along three dimensions: Directness vs. Indirectness, Positive Face Saving, and Negative Face Saving, and define Overall Politeness as their average. We formulate face-act tagging as a multi-label classification task and compare context-agnostic sentence classifiers to context-sensitive variants and sequence-labeling models. We also model document-level politeness via multi-target regression using the text, and enhance the regression model with the predictions of our face-act classifier. We show that context-sensitive classifiers outperform context-free classifiers for face-act tagging. Moreover, we demonstrate that for document-level politeness regression, enhancing the text with face-act predictions yields better performance. Our code and data are available online: alipanahroshad-oss/email-politeness-corpus