arXiv (Cornell University) · 2019 · 20 citations · 7 references
ComputingComputer ScienceComputational LinguisticsNatural Language ProcessingText AnalyticsAffective ComputingDialogue AnalysisDialogue SystemsEmotion RecognitionEmotional ExpressionSocial MediaEmotion AnnotationEmotion Dataset ConstructionEmotion DetectionEmotion Recognition From SpeechEmotion Recognition In InteractionText-based Emotion AnalysisText-based Emotion DetectionText-based Emotion Recognition
We present an overview of the EmotionX 2019 Challenge, held at the 7th International Workshop on Natural Language Processing for Social Media (SocialNLP), in conjunction with IJCAI 2019. The challenge entailed predicting emotions in spoken and chat-based dialogues using augmented EmotionLines datasets. EmotionLines contains two distinct datasets: the first includes excerpts from a US-based TV sitcom episode scripts (Friends) and the second contains online chats (EmotionPush). A total of thirty-six teams registered to participate in the challenge. Eleven of the teams successfully submitted their predictions performance evaluation. The top-scoring team achieved a micro-F1 score of 81.5% for the spoken-based dialogues (Friends) and 79.5% for the chat-based dialogues (EmotionPush).
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EmotionLines: An Emotion Corpus of Multi-Party Conversations
Sheng-Yeh Chen, Chao-Chun Hsu, Chuan-Chun Kuo et al. · arXiv (Cornell University) · 2018 · 84 citations · Full text
EmotionX-IDEA: Emotion BERT -- an Affectional Model for Conversation
Yen-Hao Huang, Ssu-Rui Lee, Mau-Yun Ma et al. · arXiv (Cornell University) · 2019 · 38 citations · Full text