Humans sometimes anthropomorphize everyday objects, but especially robots that have human-like qualities and that are often able to interact with and respond to humans in ways that other objects cannot. Humans especially attribute emotion to robot behaviors, partly because humans often use and interpret emotions when interacting with other humans, and they apply that capability when interacting with robots. Moreover, emotions are a fundamental part of the human language system and emotions are used as scaffolding for language learning, making them an integral part of language learning and meaning. However, there are very few datasets that explore how humans perceive the emotional states of robots and how emotional behaviors relate to human language. To address this gap we have collected HADREB, a dataset of human appraisals and English descriptions of robot emotional behaviors collected from over 30 participants. These descriptions and human emotion appraisals are collected using the Mistyrobotics Misty II and the Digital Dream Labs Cozmo (formerly Anki) robots. The dataset contains English descriptions and emotion appraisals of more than 500 descriptions and graded valence labels of 8 emotion pairs for each behavior and each robot. In this paper we describe the process of collecting and cleaning the data, give a general analysis of the data, and evaluate the usefulness of the dataset in two experiments, one using a language model to map descriptions to emotions, the other maps robot behaviors to emotions.