Sentiment Analysis Task

Sentiment analysis aims to automatically determine the emotional tone expressed in text, speech, or other modalities, primarily focusing on classifying sentiment as positive, negative, or neutral. Current research emphasizes improving accuracy and robustness across diverse languages and domains, employing models like BERT and other transformer architectures, along with techniques such as contrastive learning and data augmentation to address challenges like limited data and nuanced language. This field is crucial for understanding public opinion, improving customer service, and enhancing the transparency and fairness of AI systems in various applications.