# Are Word Embedding-based Features Useful for Sarcasm Detection?

arXiv: https://arxiv.org/abs/1610.00883 · HTML: https://mlsanity.com/paper/1610.00883/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Oct 2016.

## Built on by (most cited first, 14 of 14)

- [A Survey on Green Deep Learning](https://mlsanity.com/paper/2111.05193/) (2021)
- [AI-enabled Automation for Completeness Checking of Privacy Policies](https://mlsanity.com/paper/2106.05688/) (2021)
- [Sarcasm Detection: A Comparative Study](https://mlsanity.com/paper/2107.02276/) (2021)
- [Can Large Vision-Language Models Understand Multimodal Sarcasm?](https://mlsanity.com/paper/2508.03654/) (2025)
- [InterCLIP-MEP: Interactive CLIP and Memory-Enhanced Predictor for Multi-modal Sarcasm Detection](https://mlsanity.com/paper/2406.16464/) (2024)
- [A Survey on Automated Sarcasm Detection on Twitter](https://mlsanity.com/paper/2202.02516/) (2022)
- [Multi-Task Text Classification using Graph Convolutional Networks for Large-Scale Low Resource Language](https://mlsanity.com/paper/2205.01204/) (2022)
- [VyAnG-Net: A Novel Multi-Modal Sarcasm Recognition Model by Uncovering Visual, Acoustic and Glossary Features](https://mlsanity.com/paper/2408.10246/) (2024)
- [Computational Sarcasm Analysis on Social Media: A Systematic Review](https://mlsanity.com/paper/2209.06170/) (2022)
- [Modelling Visual Semantics via Image Captioning to extract Enhanced Multi-Level Cross-Modal Semantic Incongruity Representation with Attention for Multimodal Sarcasm Detection](https://mlsanity.com/paper/2408.02595/) (2024)
- ["When Words Fail, Emojis Prevail": Generating Sarcastic Utterances with Emoji Using Valence Reversal and Semantic Incongruity](https://mlsanity.com/paper/2305.04105/) (2023)
- [Sarcasm Detection in a Disaster Context](https://mlsanity.com/paper/2308.08156/) (2023)
- [Dual Modality-Aware Gated Prompt Tuning for Few-Shot Multimodal Sarcasm Detection](https://mlsanity.com/paper/2507.04468/) (2025)
- [Semi-automatic WordNet Linking using Word Embeddings](https://mlsanity.com/paper/2201.01747/) (2022)

## Abstract (arXiv)

This paper makes a simple increment to state-of-the-art in sarcasm detection research. Existing approaches are unable to capture subtle forms of context incongruity which lies at the heart of sarcasm. We explore if prior work can be enhanced using semantic similarity/discordance between word embeddings. We augment word embedding-based features to four feature sets reported in the past. We also experiment with four types of word embeddings. We observe an improvement in sarcasm detection, irrespective of the word embedding used or the original feature set to which our features are augmented. For example, this augmentation results in an improvement in F-score of around 4% for three out of these four feature sets, and a minor degradation in case of the fourth, when Word2Vec embeddings are used. Finally, a comparison of the four embeddings shows that Word2Vec and dependency weight-based features outperform LSA and GloVe, in terms of their benefit to sarcasm detection.