Data representation methods and machine learning algorithms for audio-based sentiment and emotion analysis

Audio-based sentiment and emotion analysis is an increasingly important area of investigation due to its broad applications in health, business, education, and human-computer interactions. Audio input data can be represented as transcribed texts, acoustic features, spectrograms, and multimodal representations for audio-based sentiment and emotion analysis. This paper surveys various techniques and algorithms used for audio-based sentiment and emotion analysis, exhibiting the benefits and limitations of different data representations. Additionally, we identify representative datasets for audio-based sentiment and emotion analysis. The primary contributions of this paper include a structured review of data representations for audio-based sentiment and emotion analysis, as well as an exploration of the suitability of these audio data representations for different tasks. By presenting a comprehensive overview of the state-of-the-art data representation methods in audio-based sentiment and emotion analysis, this paper aims to facilitate advancements in the field and inspire new research directions. This work offers a valuable resource for researchers, practitioners, and professionals interested in understanding and harnessing the power of audio-based sentiment and emotion analysis.