Targeted Sentiment Analysis

Targeted sentiment analysis (TSA) focuses on identifying the sentiment expressed towards specific entities or aspects within a text, going beyond overall sentiment classification. Current research heavily utilizes large language models (LLMs), particularly fine-tuned transformer architectures like BERT and its variants, along with innovative approaches like chain-of-thought prompting and graph convolutional networks to improve accuracy and handle diverse data sources, including news headlines and social media posts. This field is crucial for understanding nuanced opinions in various domains, impacting applications such as brand monitoring, political analysis, and improving the performance of AI systems that interact with human language. The development of robust, multi-domain TSA models capable of handling diverse languages and data formats remains a key area of ongoing investigation.