A Comparative Performance Evaluation of Algorithms for the Analysis and Recognition of Emotional Content

Abstract

Sentiment Analysis is highly valuable in Natural Language Processing (NLP) across domains, processing and evaluating sentiment in text for emotional understanding. This technology has diverse applications, including social media monitoring, brand management, market research, and customer feedback analysis. Sentiment Analysis identifies positive, negative, or neutral sentiments, providing insights into decision-making, customer experiences, and business strategies. With advanced machine learning models like Transformers, Sentiment Analysis achieves remarkable progress in sentiment classification. These models capture nuances, context, and variations for more accurate results. In the digital age, Sentiment Analysis is indispensable for businesses, organizations, and researchers, offering deep insights into opinions, sentiments, and trends. It impacts customer service, reputation management, brand perception, market research, and social impact analysis. In the following experimental research, we will examine the Zero-Shot technique on pre-trained Transformers and observe that, depending on the Model we use, we can achieve up to 83% in terms of the model’s ability to distinguish between classes in this Sentiment Analysis problem.