MEISD: A Multimodal Multi-Label Emotion, Intensity and Sentiment Dialogue Dataset for Emotion Recognition and Sentiment Analysis in Conversations

Mauajama Firdaus, Hardik Chauhan, Asif Ekbal, Pushpak Bhattacharyya

Abstract:

Emotion and sentiment classification in dialogues is a challenging task that has gained popularity
in recent times. Humans tend to have multiple emotions with varying intensities while expressing
their thoughts and feelings. Emotions in an utterance of dialogue can either be independent or
dependent on the previous utterances, making the task complex and interesting. Multi-label
emotion detection in conversations is a significant task that provides the ability to the system to
understand the various emotions of the users interacting. On the other hand, sentiment analysis
in dialogue or conversation helps in understanding the perspective of the user with respect to
the ongoing conversation. Besides text, additional information in the form of audio and video
assists in identifying the correct emotions with the appropriate intensity and sentiments in an
utterance of a dialogue. Lately, quite a few datasets have been made available for emotion and
sentiment classification in dialogues. Still, these datasets are imbalanced in representing different
emotions and consist of only a single emotion. Hence, we present at first a large-scale balanced
Multimodal Multi-label Emotion, Intensity, and Sentiment Dialogue dataset (MEISD) collected
from different TV series that has textual, audio, and visual features, and then establish a baseline
setup for further research