  Feeling emotion is a critical characteristic to distinguish people from
machines. Among all the multi-modal resources for emotion detection, textual
datasets are those containing the least additional information in addition to
semantics, and hence are adopted widely for testing the developed systems.
However, most of the textual emotional datasets consist of emotion labels of
only individual words, sentences or documents, which makes it challenging to
discuss the contextual flow of emotions. In this paper, we introduce
EmotionLines, the first dataset with emotions labeling on all utterances in
each dialogue only based on their textual content. Dialogues in EmotionLines
are collected from Friends TV scripts and private Facebook messenger dialogues.
Then one of seven emotions, six Ekman's basic emotions plus the neutral
emotion, is labeled on each utterance by 5 Amazon MTurkers. A total of 29,245
utterances from 2,000 dialogues are labeled in EmotionLines. We also provide
several strong baselines for emotion detection models on EmotionLines in this
paper.
