Introduction
In recent years, online social media platforms have become saturated with diverse expressions of free speech, leading to heightened attention on sarcasm detection as a specialized form of sentiment analysis. Sarcasm, a rhetorical device that conveys meaning through irony and contradiction, is widely used in product reviews, political discourse, and everyday social interactions. However, its implicit nature—often masking humor, criticism, or disdain—poses significant challenges for automated sentiment analysis and opinion mining.
Sarcasm detection (SD) aims to develop computational models capable of identifying sarcastic expressions in text, images, or multimodal content. Accurate detection is crucial for deciphering nuanced public sentiment, improving content moderation, and enhancing human-computer interaction. Early research relied on linguistic rules and traditional machine learning, but recent advancements in deep learning have revolutionized the field by enabling context-aware and multimodal approaches.
This article provides a systematic review of sarcasm detection in social media, covering datasets, methodologies, and applications. We first categorize existing datasets based on language, modality, and source. Next, we explore traditional and deep learning-based approaches for textual sarcasm detection, emphasizing the role of context, external knowledge, and auxiliary tasks. We then discuss multimodal sarcasm detection, highlighting attention mechanisms, pre-trained models, graph neural networks, and quantum neural networks. Finally, we examine real-world applications and outline future research directions.
Sarcasm Detection Datasets
The quality and diversity of datasets significantly influence the performance of sarcasm detection models. Existing datasets vary in language, modality, and collection sources.
Data Sources
Twitter and Reddit are the most common sources for short-text sarcasm datasets. Twitter datasets often use hashtags like #sarcasm or #irony for self-annotation, while Reddit datasets leverage platform-specific markers such as “/s” to denote sarcasm. For long-text sarcasm, datasets like the Internet Argument Corpus (IAC) and news headline collections provide more formal and context-rich samples.
Modalities
Early sarcasm detection focused solely on text. However, modern datasets incorporate multiple modalities, including images, audio, and video. For example, the Multimodal Sarcasm Dataset (MMSD) includes text, images, and image attributes, while MUStARD combines text, audio, and visual cues from TV shows. These multimodal datasets enable models to capture incongruities between text and accompanying media, a key indicator of sarcasm.
Languages
Most datasets are in English, but recent efforts have expanded to Arabic, Hindi, Chinese, and code-mixed languages (e.g., Hinglish). Cross-lingual sarcasm detection remains challenging due to cultural and linguistic nuances, necessitating language-specific models and resources.
Textual Sarcasm Detection
Textual sarcasm detection methods have evolved from rule-based systems to sophisticated deep learning models.
Traditional Methods
Early approaches relied on handcrafted linguistic rules, such as identifying emotional inconsistency, exaggerated language, or punctuation patterns (e.g., excessive exclamation marks). Machine learning methods later automated feature extraction using techniques like TF-IDF, n-grams, and sentiment lexicons. Algorithms such as Support Vector Machines (SVM) and logistic regression were commonly used for classification.
While interpretable, these methods struggled with generalization and required extensive feature engineering.
Deep Learning Approaches
Deep learning models, particularly those based on recurrent and attention mechanisms, have significantly improved sarcasm detection.
Sentence-Level Detection
Models like Bidirectional LSTMs (BiLSTMs) and Transformers capture long-range dependencies and contextual cues. Multi-head attention mechanisms help identify key words or phrases that signal sarcasm, such as incongruent sentiment within a sentence.
Context-Augmented Detection
Sarcasm often relies on contextual clues beyond the target text. For example:
- User History: Models like CASCADE incorporate user behavior and past posts to detect sarcastic tendencies.
- Conversational Context: Graph Attention Networks (GATs) model social interactions to infer sarcasm in dialogues.
- External Knowledge: Commonsense knowledge graphs (e.g., ConceptNet) or event-based reasoning (e.g., COMET) enrich text representations with implicit meaning.
Multitask Learning
Jointly training models for sarcasm detection and related tasks (e.g., sentiment analysis or emotion recognition) improves performance by leveraging shared linguistic features.
Multimodal Sarcasm Detection
Multimodal sarcasm detection leverages visual, auditory, and textual cues to identify irony. Key methodologies include:
Attention Mechanisms
- Cross-Modal Attention: Aligns text and image features to detect contradictions (e.g., positive text paired with negative imagery).
- Co-Attention: Computes joint representations of text and image attributes to highlight incongruities.
- Contrastive Attention: Focuses on mismatches between modalities, such as sarcastic tone in audio versus neutral text.
Pre-Trained Models
Models like BERT, ViT, and CLIP provide robust feature extraction for text and images. For example:
- ViLBERT: Processes text and images in separate streams before fusing them for sarcasm prediction.
- CLIP: Uses contrastive learning to align visual and textual semantics, improving generalization.
Graph Neural Networks (GNNs)
GNNs model relationships between modalities or contextual elements:
- Graph Convolutional Networks (GCNs): Construct sentiment or dependency graphs to detect emotional inconsistency.
- Graph Isomorphism Networks (GINs): Aggregate multimodal features dynamically, enhancing fusion.
Quantum Neural Networks (QNNs)
Emerging quantum-inspired models, such as Quantum Fuzzy Neural Networks (QFNNs), use complex-valued representations to handle the uncertainty and ambiguity of sarcastic expressions. While experimental, these methods show promise for capturing subtle cues.
Applications of Sarcasm Detection
- Content Moderation: Identifying sarcasm helps filter harmful or misleading content while preserving humor.
- Customer Feedback Analysis: Businesses detect sarcastic reviews to address genuine complaints.
- Mental Health Monitoring: Sarcasm can indicate stress or depression, aiding early intervention.
- Dialogue Systems: Virtual assistants respond appropriately to sarcastic queries, improving user experience.
Challenges and Future Directions
Despite progress, challenges remain:
- Cultural Variability: Sarcasm expressions differ across languages and regions.
- Data Scarcity: Annotated datasets, especially for low-resource languages, are limited.
- Explainability: Deep learning models often lack transparency in decision-making.
Future research should explore:
- Fine-Grained Labels: Differentiating sarcasm types (e.g., self-deprecating vs. mocking).
- Novel Modalities: Incorporating emojis, gestures, or sensory data (e.g., tone of voice).
- Large Language Models (LLMs): Leveraging models like GPT-4 for zero-shot or few-shot sarcasm detection.
- Quantum Computing: Scaling QNNs for real-world deployment.
Conclusion
Sarcasm detection is a rapidly evolving field bridging NLP, computer vision, and social computing. From rule-based systems to quantum-inspired models, advancements have enabled more accurate and nuanced detection. As social media continues to grow, robust sarcasm detection will play a pivotal role in sentiment analysis, content moderation, and human-AI interaction.
DOI: 10.19734/j.issn.1001-3695.2024.08.0317
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