# Sarcasm Detection in Tweets with BERT and GloVe Embeddings

arXiv: https://arxiv.org/abs/2006.11512 · HTML: https://mlsanity.com/paper/2006.11512/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jun 2020.

## Built on by (most cited first, 8 of 8)

- [A Review of the Trends and Challenges in Adopting Natural Language Processing Methods for Education Feedback Analysis](https://mlsanity.com/paper/2301.08826/) (2023)
- [An Evaluation of State-of-the-Art Large Language Models for Sarcasm Detection](https://mlsanity.com/paper/2312.03706/) (2023)
- [Computational Sarcasm Analysis on Social Media: A Systematic Review](https://mlsanity.com/paper/2209.06170/) (2022)
- [X-PuDu at SemEval-2022 Task 6: Multilingual Learning for English and Arabic Sarcasm Detection](https://mlsanity.com/paper/2211.16883/) (2022)
- [Identification of Potentially Misclassified Crash Narratives using Machine Learning (ML) and Deep Learning (DL)](https://mlsanity.com/paper/2507.03066/) (2025)
- [Researchers eye-view of sarcasm detection in social media textual content](https://mlsanity.com/paper/2304.08582/) (2023)
- [QiBERT -- Classifying Online Conversations Messages with BERT as a Feature](https://mlsanity.com/paper/2409.05530/) (2024)
- [An Innovative CGL-MHA Model for Sarcasm Sentiment Recognition Using the MindSpore Framework](https://mlsanity.com/paper/2411.01264/) (2024)

## Abstract (arXiv)

Sarcasm is a form of communication in whichthe person states opposite of what he actually means. It is ambiguous in nature. In this paper, we propose using machine learning techniques with BERT and GloVe embeddings to detect sarcasm in tweets. The dataset is preprocessed before extracting the embeddings. The proposed model also uses the context in which the user is reacting to along with his actual response.