用传统机器学习和手工特征检测贴吧讽刺语，效果稳定可复现。
Sarcasm Detection on Reddit Using Classical Machine Learning and Feature Engineering
- 结合词级、字符级TF-IDF与风格特征，不依赖神经网络
- 朴素贝叶斯与逻辑回归F1达0.57，表现最佳
- 适合需要可解释性的轻量级讽刺识别场景
讽刺在在线讨论中常见，但机器难以识别，因其语义常与字面意思相反。本文仅使用经典机器学习方法与显式特征工程进行讽刺检测，未依赖神经网络或父评论上下文。基于10万条评论的Self-Annotated Reddit Corpus (SARC 2.0)子样本，融合词级与字符级TF-IDF特征及简单风格指标，评估了四种模型：逻辑回归、线性SVM、多项式朴素贝叶斯和随机森林。朴素贝叶斯与逻辑回归表现最优，讽刺类评论的F1分数约为0.57。尽管缺乏对话上下文限制了性能，结果仍为轻量、可解释的讽刺检测提供了清晰可复现的基线。
原文摘要 · Abstract (English)
Sarcasm is common in online discussions, yet difficult for machines to identify because the intended meaning often contradicts the literal wording. In this work, I study sarcasm detection using only classical machine learning methods and explicit feature engineering, without relying on neural networks or context from parent comments. Using a 100,000-comment subsample of the Self-Annotated Reddit Corpus (SARC 2.0), I combine word-level and character-level TF-IDF features with simple stylistic indicators. Four models are evaluated: logistic regression, a linear SVM, multinomial Naive Bayes, and a random forest. Naive Bayes and logistic regression perform the strongest, achieving F1-scores around 0.57 for sarcastic comments. Although the lack of conversational context limits performance, the results offer a clear and reproducible baseline for sarcasm detection using lightweight and interpretable methods.
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