Polarity Classification

Polarity classification, a core task in sentiment analysis, aims to determine the positive, negative, or neutral sentiment expressed in text. Current research emphasizes improving accuracy and efficiency across diverse languages and domains, employing deep learning models like Convolutional Neural Networks (CNNs) and leveraging the capabilities of large language models (LLMs) such as GPT-3.5 and GPT-4, often within multi-task learning frameworks that simultaneously address related tasks like aspect extraction. These advancements are crucial for applications ranging from customer feedback analysis to cross-cultural understanding, enabling more nuanced and accurate interpretation of textual data.