mlsanityData to 7 Oct 2026

Sarcasm Detection in Tweets with BERT and GloVe Embeddings

arXiv 2006.11512 ↗First posted 20 Jun 2020cs.CL cs.LGcited by 8

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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.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Jun 2020.

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Citing papers per year: 2022: 2; 2023: 3; 2024: 2; 2025: 1.

8 papers.

  1. 2023A Review of the Trends and Challenges in Adopting Natural Language Processing Methods for Education Feedback Analysiscited by 12
  2. 2023An Evaluation of State-of-the-Art Large Language Models for Sarcasm Detectioncited by 3
  3. 2022Computational Sarcasm Analysis on Social Media: A Systematic Reviewcited by 2
  4. 2022X-PuDu at SemEval-2022 Task 6: Multilingual Learning for English and Arabic Sarcasm Detectioncited by 2
  5. 2025Identification of Potentially Misclassified Crash Narratives using Machine Learning (ML) and Deep Learning (DL)cited by 1
  6. 2023Researchers eye-view of sarcasm detection in social media textual contentcited by 0
  7. 2024QiBERT -- Classifying Online Conversations Messages with BERT as a Featurecited by 0
  8. 2024An Innovative CGL-MHA Model for Sarcasm Sentiment Recognition Using the MindSpore Frameworkcited by 0