Exploring Linguistic and Emotional Models for Audio Sentiment Analysis Using NLP

Abstract

Sentiment analysis is widely used to identify emotions and attitudes in text. With the growing popularity of audio-based social platforms and the significant rise in spoken data, sentiment analysis in the auditory domain has become increasingly important. This paper explores sentiment analysis in audio data using Natural Language Processing (NLP) techniques. We propose a novel method for extracting linguistic features and developing emotional models tailored to audio-based sentiment. In our experiments, we compare deep learning models with traditional NLP techniques, using a unique dataset to validate our findings. Sentiment analysis, also known as opinion mining, is a key subfield of NLP, focusing on extracting subjective information from textual data. The surge of user-generated content on online platforms like social media, blogs, and product reviews has amplified the importance of sentiment analysis for understanding public opinion and consumer behavior. This paper provides an overview of various approaches used in sentiment analysis, including machine learning, lexicon-based methods, and deep learning, highlighting their strengths and limitations. We discuss the trade-offs between accuracy, computational efficiency, and interpretability for each approach, while addressing challenges like sarcasm detection, context dependency, and domain-specific language. Additionally, we examine recent advancements in the field, such as the use of cross-sectional models and the integration of multiple data sources to provide a more comprehensive view of sentiment. Our results demonstrate the efficiency and high performance of the proposed models in capturing sentiment from audio data. The study also explores the ethical considerations, practical applications, and broader relevance of audio-based sentiment analysis across media and other domains. Finally, we conclude by discussing future directions, emphasizing the need for more robust models capable of handling diverse and complex data, along with ethical considerations for real-world applications.