Sentence Level Sentiment

Sentence-level sentiment analysis focuses on identifying the sentiment expressed within individual sentences, a crucial step in understanding the overall sentiment of longer texts. Current research emphasizes improving accuracy and addressing challenges like the inconsistent relationship between sentence-level and overall text sentiment, often employing deep learning models such as transformers and recurrent neural networks (RNNs) with attention mechanisms. This granular approach is vital for applications ranging from financial market analysis (gauging investor sentiment from news articles) to product review analysis and improving the interpretability of large language models.