Document Level Sentiment

Document-level sentiment analysis focuses on determining the overall sentiment expressed in a text, such as a review or social media post, rather than analyzing sentiment at the sentence or aspect level. Current research emphasizes efficient methods, including exploring various feature extraction techniques, ensemble models, and deep learning architectures like transformer-based models (e.g., DeBERTa) and variational autoencoders (VAEs), while also considering resource constraints for practical deployment. This field is crucial for applications ranging from customer feedback analysis and brand reputation management to mental health monitoring via social media, offering valuable insights into public opinion and individual emotional states.