What are the approaches to sentiment analysis beyond simple positive/negative classification?

Modern sentiment analysis goes beyond binary polarity to capture nuanced opinion. Levels: document-level (overall sentiment of a review or article), sentence-level (sentiment per sentence), aspect-level (sentiment toward specific entities or features, e.g., "The food was great but service was slow" = positive:food, negative:service), and emotion detection (classify into specific emotions: joy, anger, sadness, fear, surprise, disgust). Aspect-Based Sentiment Analysis (ABSA): extract aspect terms (features mentioned in the text), classify aspect categories (group aspects into predefined categories like food, service, ambiance), determine sentiment polarity per aspect, and optionally extract opinion terms (the words expressing the sentiment). Implementation: fine-tune pre-trained models on ABSA datasets (SemEval restaurant/laptop reviews), use instruction-tuned LLMs for zero-shot ABSA with structured output, or pipeline approach (NER for aspects, classification for sentiment). Advanced: sarcasm detection (often inverts sentiment, requires understanding context and tone), implicit sentiment (no explicit opinion words but sentiment is implied, "The battery lasted only 2 hours"), and comparative sentiment ("Product A is better than Product B"). Multimodal sentiment: combine text with images (visual sentiment), audio features (tone of voice, speech patterns), and video (facial expressions, gestures) for comprehensive sentiment understanding. Production considerations: handle negation and modifiers ("not bad" = positive, "very disappointing" = strong negative), domain adaptation (sentiment vocabulary differs across domains), and temporal analysis (track sentiment changes over time for brand monitoring). Evaluation: accuracy and F1 for classification, and for ABSA measure both aspect extraction accuracy and aspect sentiment accuracy.