Implicit Sentiment

Implicit sentiment analysis focuses on identifying sentiment not explicitly expressed through obvious cue words, aiming to understand nuanced emotional cues and opinions within text. Current research heavily utilizes large language models (LLMs), such as BERT, GPT variants, and others, often incorporating techniques like chain-of-thought prompting, graph convolutional networks, and ensemble methods to improve accuracy and address challenges like sarcasm and context-dependent meaning. This field is significant for its applications across diverse domains, including social media monitoring, market research, healthcare (e.g., analyzing patient feedback), and improving human-computer interaction by enabling more nuanced understanding of user sentiment.