A Cross-Domain Exploration of Audio and Textual Data for Multi-Modal Emotion Detection

The field of sentiment and emotion analysis is a challenging problem that has received research attention. The complexity of emotion and sentiment recognition draws from variability in expression, cultural and individual differences, context dependency, etc. This work takes an exploratory approach to the problem by performing an extensive classification of emotion using machine learning (ML) applied to textual and auditory data sources. We create a pipeline that facilitates the examination of textual and auditory inputs, resulting in more reliable emotional classification. The study uses multiple audio and textual datasets for the prediction of four distinct emotions. A four-layer Bi-LSTM model achieved 95% accuracy in emotion analysis from auditory clips. The training set contained 2391 samples, with Angry (20%), Fearful (18%), Happy (38%), and Neutral (24%). In the validation set of 713 samples, emotions were similarly distributed. The test set had 312 samples, with percentages of emotions comparable to the training set. We merged four datasets for textual analysis and utilized the "emotion english distilroberta base" model 5, achieving 90% accuracy on the test data. In the training set, emotions were distributed as follows: Angry (25%), Fearful (23%), Happy (23%), and Neutral (29%). The validation set comprised 305 samples, with similar distributions across emotions. The test set consisted of 712 samples, with percentages of emotions similar to the training set. We develop an application that combines both classifications to obtain a robust classification of arbitrary audio tracks.