When Implicit Offence Is Not Necessarily Negative: Pragmatic Strategies and Sentiment Polarity in Chinese Online Attacks

This study investigates how sentiment polarity relates to offensive function in Chinese online attacks, focusing on cases where offence is expressed implicitly rather than through explicit negative wording. Using a 1400-item Chinese online-text corpus derived from TOXICN and its pragmatics-oriented annotation extension, the study examines sentiment distributions across non-offensive texts, explicit attacks, and implicit attacks, and evaluates how these patterns are reflected in model-generated sentiment labels. The implicit-attack subset is analyzed through four pragmatic strategies: Irony, Trope, Indirectness, and Exaggeration. A stratified 400-item validation subset was independently annotated by three human annotators for sentiment polarity, and four NLP models were used to assign sentiment labels to the full corpus: Claude Haiku 4.5, OpenAI GPT-4.1-mini, a Chinese BERT-based sentiment classifier, and Multilingual DistilBERT. Human reference annotations showed that negative sentiment was strongly associated with offensive-language labels but did not map onto them perfectly: some non-offensive texts were perceived as negative, while some implicit attacks were assigned neutral sentiment. Pragmatic strategy did not significantly predict human reference sentiment polarity in the validation subset. Model–human agreement varied across the four selected systems, with Claude Haiku 4.5 and OpenAI GPT-4.1-mini showing higher agreement than Chinese BERT and Multilingual DistilBERT in the validation subset. Full-corpus mixed-effects logistic regression showed that model-generated negative labels varied significantly by text category and model, whereas evidence for stable model-specific pragmatic-strategy effects was limited. These findings provide empirical evidence from Chinese online attacks that sentiment polarity can inform, but cannot replace, offensive-language analysis. They also show that model-generated sentiment labels require human validation and cautious interpretation when offence is implicit, figurative, homophonic, or context-dependent.