In sentiment analysis, how to handle complex semantics such as sarcasm and irony?

In sentiment analysis, handling complex semantics such as sarcasm and irony usually requires combining contextual semantic understanding, sentiment polarity reversal detection, and multimodal information assistance. The literal sentiment of such expressions often contradicts the actual intention, necessitating breaking through single-text lexical analysis and relying on deep contextual logic. Specific methods include: - Contextual semantic analysis: Identify differences between literal and actual sentiment by recognizing转折词 (e.g., "but", "however"), hyperbolic modifiers (e.g., "That's great, not even the basic functions work"), or specific contextual markers (e.g., ironic usage in internet slang). - Sentiment polarity reversal models: Use pre-trained language models like BERT, trained on annotated ironic corpora, to capture language patterns where "positive words are used negatively" or "negative words are used positively", such as the special meaning of quotation marks in "You are such a 'little clever ghost'". - Multimodal feature integration: Assist judgment by combining emojis in the text (e.g., "👍" paired with negative descriptions), punctuation marks (e.g., the rhetorical question "Isn't this a 'good idea'?"), or social scenarios (e.g., ironic contexts in comment section interactions). In practical applications, consider optimizing models with domain-specific corpora (e.g., social media, literary works) and improving the accuracy of complex semantic recognition through manual verification, which is crucial for enhancing the reliability of sentiment analysis results.