Entity Level Sentiment

Entity-level sentiment analysis focuses on determining the sentiment expressed towards specific entities (people, organizations, products) within a text, going beyond simple overall sentiment assessment. Current research emphasizes the challenges of aggregating sentence-level sentiments to accurately reflect the overall entity sentiment, exploring the use of large language models (LLMs) and transformer-based architectures like BERT and its variants to improve accuracy, often incorporating techniques like chain-of-thought prompting and semi-supervised learning. This field is crucial for various applications, including understanding public opinion in political contexts, improving financial market analysis, and enhancing biomedical literature analysis, driving the development of new datasets and evaluation benchmarks.