Emotion classification

Social psychology

Economics

Computer science

Natural language processing

Utterance

Psychology

Task (project management)

Management

Emotion recognition

Multimodal learning

Modalities

Sociology

Artificial intelligence

Information retrieval

Sentiment analysis

Popularity

Speech recognition

Social science

Emotion recognition in conversations is a challenging task that has recently gained popularity due to its potential applications. Until now, however, a large-scale multimodal multi-party emotional conversational database containing more than two speakers per dialogue was missing. Thus, we propose the Multimodal EmotionLines Dataset (MELD), an extension and enhancement of EmotionLines. MELD contains about 13,000 utterances from 1,433 dialogues from the TV-series Friends. Each utterance is annotated with emotion and sentiment labels, and encompasses audio, visual and textual modalities. We propose several strong multimodal baselines and show the importance of contextual and multimodal information for emotion recognition in conversations. The full dataset is available for use at http://affective-meld.github.io.

MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations