# A Dual-Channel Framework for Sarcasm Recognition by Detecting Sentiment Conflict

arXiv: https://arxiv.org/abs/2109.03587 · HTML: https://mlsanity.com/paper/2109.03587/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 51 references that are in the map.
- First posted on arXiv 8 Sep 2021.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm](https://mlsanity.com/paper/1708.00524/) (2017)
- [A Large Self-Annotated Corpus for Sarcasm](https://mlsanity.com/paper/1704.05579/) (2017)
- [Creating and Characterizing a Diverse Corpus of Sarcasm in Dialogue](https://mlsanity.com/paper/1709.05404/) (2017)
- [Reasoning with Sarcasm by Reading In-between](https://mlsanity.com/paper/1805.02856/) (2018)
- [A Deeper Look into Sarcastic Tweets Using Deep Convolutional Neural Networks](https://mlsanity.com/paper/1610.08815/) (2016)
- [CASCADE: Contextual Sarcasm Detection in Online Discussion Forums](https://mlsanity.com/paper/1805.06413/) (2018)
- [A Report on the 2020 Sarcasm Detection Shared Task](https://mlsanity.com/paper/2005.05814/) (2020)
- [GCDT: A Global Context Enhanced Deep Transition Architecture for Sequence Labeling](https://mlsanity.com/paper/1906.02437/) (2019)
- [Automatic Sarcasm Detection: A Survey](https://mlsanity.com/paper/1602.03426/) (2016)
- [Really? Well. Apparently Bootstrapping Improves the Performance of Sarcasm and Nastiness Classifiers for Online Dialogue](https://mlsanity.com/paper/1708.08572/) (2017)
- [Representing Social Media Users for Sarcasm Detection](https://mlsanity.com/paper/1808.08470/) (2018)
- [Unsupervised Learning for Lexicon-Based Classification](https://mlsanity.com/paper/1611.06933/) (2016)

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

- [FLM-101B: An Open LLM and How to Train It with 100K Budget](https://mlsanity.com/paper/2309.03852/) (2023)
- [SarcasmBench: Towards Evaluating Large Language Models on Sarcasm Understanding](https://mlsanity.com/paper/2408.11319/) (2024)
- [A Survey of Multimodal Sarcasm Detection](https://mlsanity.com/paper/2410.18882/) (2024)
- [FanChuan: A Multilingual and Graph-Structured Benchmark For Parody Detection and Analysis](https://mlsanity.com/paper/2502.16503/) (2025)
- [CAF-I: A Collaborative Multi-Agent Framework for Enhanced Irony Detection with Large Language Models](https://mlsanity.com/paper/2506.08430/) (2025)
- [Computational Sarcasm Analysis on Social Media: A Systematic Review](https://mlsanity.com/paper/2209.06170/) (2022)
- [BNS-Net: A Dual-channel Sarcasm Detection Method Considering Behavior-level and Sentence-level Conflicts](https://mlsanity.com/paper/2309.03658/) (2023)
- [SEVADE: Self-Evolving Multi-Agent Analysis with Decoupled Evaluation for Hallucination-Resistant Irony Detection](https://mlsanity.com/paper/2508.06803/) (2025)
- [Context-Aware Pragmatic Metacognitive Prompting for Sarcasm Detection](https://mlsanity.com/paper/2511.21066/) (2025)
- [World model inspired sarcasm reasoning with large language model agents](https://mlsanity.com/paper/2512.24329/) (2025)
- [Dual Modality-Aware Gated Prompt Tuning for Few-Shot Multimodal Sarcasm Detection](https://mlsanity.com/paper/2507.04468/) (2025)
- [RAM-SD: Retrieval-Augmented Multi-agent framework for Sarcasm Detection](https://mlsanity.com/paper/2601.17002/) (2026)
- [When Do "More Contexts" Help with Sarcasm Recognition?](https://mlsanity.com/paper/2403.12469/) (2024)
- [Investigating Vaccine Buyer's Remorse: Post-Vaccination Decision Regret in COVID-19 Social Media Using Politically Diverse Human Annotation](https://mlsanity.com/paper/2604.09626/) (2026)

## Abstract (arXiv)

Sarcasm employs ambivalence, where one says something positive but actually means negative, and vice versa. The essence of sarcasm, which is also a sufficient and necessary condition, is the conflict between literal and implied sentiments expressed in one sentence. However, it is difficult to recognize such sentiment conflict because the sentiments are mixed or even implicit. As a result, the recognition of sophisticated and obscure sentiment brings in a great challenge to sarcasm detection. In this paper, we propose a Dual-Channel Framework by modeling both literal and implied sentiments separately. Based on this dual-channel framework, we design the Dual-Channel Network~(DC-Net) to recognize sentiment conflict. Experiments on political debates (i.e. IAC-V1 and IAC-V2) and Twitter datasets show that our proposed DC-Net achieves state-of-the-art performance on sarcasm recognition. Our code is released to support research.