Modern sentiment analysis algorithms successfully identify human emotion

Duan G, Chen Z, Shang Y, Xue H. Emotion correction for weibo users via weakly supervised learning and semantic understanding.. 2026. https://doi.org/10.1016/j.isci.2026.116101

Paper [4] shows that semantic understanding and machine learning approaches can accurately identify emotional states and polarity in user-generated text.

Satapathy P, Chauhan O, Kumar D, Sharma P, Banshal SK, Geman O, Morosan-Danila L, Hemanth JD. A unified multimodal learning framework for sentiment analysis and mental health indicators from YouTube videos.. 2026. https://doi.org/10.1007/s44192-026-00388-6

Paper [6] establishes that multimodal deep learning frameworks effectively capture nuanced human emotions and sentiment patterns from video and audio cues.

Md Zuki MA, Mohamad Ali N, Chaw JK. Multimodal Sentiment and Emotion Analysis Framework for Personalized Health Coaching Messages: Proof-of-Concept Study.. 2026. https://doi.org/10.2196/79558

Paper [8] indicates that transformer-based models achieve high accuracy in sentiment analysis and emotion detection tasks.

Li S, Li H, Du J, Yan S, Dong C. Feature fusion based transformer for sentiment analysis in social networks.. 2025. https://doi.org/10.1371/journal.pone.0333416

Paper [11] finds that feature fusion transformers successfully evaluate and classify user emotional states from complex multimodal social media content.