Sentiment Analysis

Sentiment analysis studies how people express polarity, emotion, intensity, and opinions about specific targets, supporting applications from customer feedback and public discourse to health and finance. Common approaches range from lexicons and classical classifiers to fine-tuned transformers and large language models; aspect-based, dimensional, and structured methods capture more than a single positive-or-negative label. Multimodal systems combine words with speech, prosody, facial expression, or video, while multilingual and domain-adaptive methods aim to handle differences in language, dialect, and context. Strong benchmark results coexist with persistent challenges in neutral and implicit sentiment, class imbalance, cross-domain transfer, model bias, and disagreement over what labels actually measure; prompted LLMs do not consistently outperform task-specific fine-tuning.