EmotionLines: An Emotion Corpus of Multi-Party Conversations

TL;DR

EmotionLines is a novel dataset of multi-party dialogues with utterance-level emotion labels, enabling better analysis of emotional flow in conversations for emotion detection research.

Contribution

It introduces the first dataset with emotion labels on all utterances in dialogues, based solely on textual content, from TV scripts and social media.

Findings

29,245 utterances labeled with emotions

Provides strong baseline models for emotion detection

Enables analysis of emotional flow in dialogues

Abstract

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.