Advancing Sentiment Analysis in Audio: Deep Learning and NLP Approaches for Emotion Detection in Spoken Language

Sentiment analysis, primarily used to identify emotions and attitudes in text, has now extended to the increasingly popular audio-based social platforms. This study investigates sentiment analysis in audio, utilizing Natural Language Processing (NLP) to interpret emotions in spoken language. We conducted experiments on a new dataset, employing deep learning models for sentiment analysis and comparing their performance to established NLP methods for audio. Our results show that our model is both efficient and highly effective in detecting sentiment from audio data. Additionally, we discuss the applications, ethical considerations, and future perspectives of audio-based sentiment analysis in today's media landscape. Our approach aims to advance the development of sophisticated models for emotion analysis, creating new opportunities across various fields. This abstract provides an overview of the study, highlighting the research focus, methodology, findings, and implications for audio-based emotion analysis through NLP.