EmotionLines: An Emotion Corpus of Multi-Party Conversations

By Shengli Chen, Chao-Chun Hsu, Chuan-Chun Kuo, Ting-Hao 'Kenneth' Huang, Lun-Wei Ku. International Conference on Language Resources and Evaluation, 2018

297 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/ba4c923b43360325cba984549aa3c3224863d1f6

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Citations in the corpus

Transformer models for text-based emotion detection: a review of BERT-based approaches (2021, methodology): “The EmotionLines dataset (Chen et al. 2019) consisting of two data subsets (i.e., the Friends and EmotionPush datasets) was released for the challenge; emotions were classified into four labels (i.e., neutral, joy, sadness, and anger).”
Directed Acyclic Graph Network for Conversational Emotion Recognition (2021, background): “Recently, several ERC datasets with textual data have been released (Busso et al., 2008; Schuller et al., 2012; Zahiri and Choi, 2017; Li et al., 2017; Chen et al., 2018; Poria et al., 2019b), arousing the widespread interest of NLP researchers.”
MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations (2018, methodology): “Chen et al. (2018) crawled the dialogues from each episode and grouped them into four groups ([5, 9], [10, 14], [15, 19], and [20, 24]) based on the number of utterances present in the dialogues.”
COSMIC: COmmonSense knowledge for eMotion Identification in Conversations (2020, background): “MELD (Poria et al., 2019a) is a multimodal dataset extended from the EmotionLines dataset (Chen et al., 2018).”
MMGCN: Multimodal Fusion via Deep Graph Convolution Network for Emotion Recognition in Conversation (2021, methodology): “Compared to the Emotion Lines dataset (Chen et al., 2018), MELD has three modality-aligned conversation data with higher quality.”
DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation (2019, methodology): “MELD (Poria et al., 2019a) is a multimodal emotion/sentiment classiﬁcation dataset which has been created by the extending the EmotionLines dataset (Chen et al., 2018).”
Emotion Recognition in Conversation: Research Challenges, Datasets, and Recent Advances (2019, methodology): “There are a few publicly available datasets for ERC - IEMOCAP [27], SEMAINE [35], Emotionlines [10], MELD [36], DailyDialog [28], and EmoContext [37].”