Videos can evoke a range of affective responses in viewers. The ability to predict evoked affect from a video, before viewers watch the video, can help in content creation and video recommendation. We introduce the Evoked Expressions from Videos (EEV) dataset, a large-scale dataset for studying viewer responses to videos. Each video is annotated at 6 Hz with 15 continuous evoked expression labels, corresponding to the facial expression of viewers who reacted to the video. We use an expression recognition model within our data collection framework to achieve scalability. In total, there are 8 million annotations of viewer facial reactions to 5,153 videos (370 hours). We use YouTube to obtain a diverse set of video content. We hope that the size and diversity of the EEV dataset will encourage further explorations in video understanding and affective computing.