Exploring the contextual factors affecting multimodal emotion recognition in videos

TL;DR

This study investigates how visual, vocal, and textual cues combined with contextual factors like gender and video length influence the accuracy of multimodal emotion recognition in videos, revealing significant variations across different conditions.

Contribution

It provides a comprehensive analysis of the impact of gender and duration on multimodal emotion recognition performance, highlighting the importance of context-aware approaches.

Findings

Multimodal features outperform unimodal and bimodal features in emotion recognition.

Performance varies significantly with gender, favoring male speakers.

Shorter videos yield better recognition accuracy for neutral and happiness emotions.

Abstract

Emotional expressions form a key part of user behavior on today's digital platforms. While multimodal emotion recognition techniques are gaining research attention, there is a lack of deeper understanding on how visual and non-visual features can be used to better recognize emotions in certain contexts, but not others. This study analyzes the interplay between the effects of multimodal emotion features derived from facial expressions, tone and text in conjunction with two key contextual factors: i) gender of the speaker, and ii) duration of the emotional episode. Using a large public dataset of 2,176 manually annotated YouTube videos, we found that while multimodal features consistently outperformed bimodal and unimodal features, their performance varied significantly across different emotions, gender and duration contexts. Multimodal features performed particularly better for male speakers in recognizing most emotions. Furthermore, multimodal features performed particularly better for shorter than for longer videos in recognizing neutral and happiness, but not sadness and anger. These findings offer new insights towards the development of more context-aware emotion recognition and empathetic systems.