  We propose a novel large-scale emotional dialogue dataset, consisting of 1M
dialogues retrieved from the OpenSubtitles corpus and annotated with 32
emotions and 9 empathetic response intents using a BERT-based fine-grained
dialogue emotion classifier. This work explains the complex pipeline used to
preprocess movie subtitles and select good movie dialogues to annotate. We also
describe the semi-supervised learning process followed to train a fine-grained
emotion classifier to annotate these dialogues. Despite the large set of
labels, our dialogue emotion classifier achieved an accuracy of $65\%$ and was
used to annotate 1M emotional movie dialogues from OpenSubtitles. This scale of
emotional dialogue classification has never been attempted before, both in
terms of dataset size and fine-grained emotion and intent categories.
Visualization techniques used to analyze the quality of the resultant dataset
suggest that it conforms to the patterns of human social interaction.
