Enhancing the Prediction of Emotional Experience in Movies using Deep
  Neural Networks: The Significance of Audio and Language
Sogand Mehrpour Mohammadi Meysam Gouran Orimi Hamidreza Rabiee
Our paper focuses on making use of deep neural network models to accurately
predict the range of human emotions experienced during watching movies. In this
certain setup, there exist three clear-cut input modalities that considerably
influence the experienced emotions: visual cues derived from RGB video frames,
auditory components encompassing sounds, speech, and music, and linguistic
elements encompassing actors' dialogues. Emotions are commonly described using
a two-factor model including valence (ranging from happy to sad) and arousal
(indicating the intensity of the emotion). In this regard, a Plethora of works
have presented a multitude of models aiming to predict valence and arousal from
video content. However, non of these models contain all three modalities, with
language being consistently eliminated across all of them. In this study, we
comprehensively combine all modalities and conduct an analysis to ascertain the
importance of each in predicting valence and arousal. Making use of pre-trained
neural networks, we represent each input modality in our study. In order to
process visual input, we employ pre-trained convolutional neural networks to
recognize scenes[1], objects[2], and actions[3,4]. For audio processing, we
utilize a specialized neural network designed for handling sound-related tasks,
namely SoundNet[5]. Finally, Bidirectional Encoder Representations from
Transformers (BERT) models are used to extract linguistic features[6] in our
analysis. We report results on the COGNIMUSE dataset[7], where our proposed
model outperforms the current state-of-the-art approaches. Surprisingly, our
findings reveal that language significantly influences the experienced arousal,
while sound emerges as the primary determinant for predicting valence. In
contrast, the visual modality exhibits the least impact among all modalities in
predicting emotions.