# Indonesian Social Media Sentiment Analysis With Sarcasm Detection

arXiv: https://arxiv.org/abs/1505.03085 · HTML: https://mlsanity.com/paper/1505.03085/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 May 2015.

## Built on by (most cited first, 5 of 5)

- [One Country, 700+ Languages: NLP Challenges for Underrepresented Languages and Dialects in Indonesia](https://mlsanity.com/paper/2203.13357/) (2022)
- [MMSD2.0: Towards a Reliable Multi-modal Sarcasm Detection System](https://mlsanity.com/paper/2307.07135/) (2023)
- [Sarcasm Detection Framework Using Context, Emotion and Sentiment Features](https://mlsanity.com/paper/2211.13014/) (2022)
- [Interpretable Bangla Sarcasm Detection using BERT and Explainable AI](https://mlsanity.com/paper/2303.12772/) (2023)
- [ViSP: A PPO-Driven Framework for Sarcasm Generation with Contrastive Learning](https://mlsanity.com/paper/2507.09482/) (2025)

## Abstract (arXiv)

Sarcasm is considered one of the most difficult problem in sentiment analysis. In our ob-servation on Indonesian social media, for cer-tain topics, people tend to criticize something using sarcasm. Here, we proposed two additional features to detect sarcasm after a common sentiment analysis is conducted. The features are the negativity information and the number of interjection words. We also employed translated SentiWordNet in the sentiment classification. All the classifications were conducted with machine learning algorithms. The experimental results showed that the additional features are quite effective in the sarcasm detection.