# Sarcasm Detection on Reddit Using Classical Machine Learning and Feature Engineering

arXiv: https://arxiv.org/abs/2512.04396 · HTML: https://mlsanity.com/paper/2512.04396/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 3 references that are in the map.
- First posted on arXiv 4 Dec 2025.

## Builds on (most cited first, 2 of 2)

- [A Survey of Multimodal Sarcasm Detection](https://mlsanity.com/paper/2410.18882/) (2024)
- [Sarcasm Analysis using Conversation Context](https://mlsanity.com/paper/1808.07531/) (2018)

## Abstract (arXiv)

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.