Source-linked AI summary
CASCADE: Contextual Sarcasm Detection in Online Discussion Forums
Devamanyu Hazarika, Soujanya Poria, Sruthi Gorantla, Erik Cambria, Roger Zimmermann, Rada Mihalcea
TL;DR
Automated sarcasm detection has largely emphasized textual cues, although implicit sarcasm can require contextual, background, and commonsense knowledge. CASCADE combines CNN-based content modeling with discourse features and user embeddings encoding stylometric and personality information, achieving state-of-the-art performance on a large Reddit corpus.
Problem
Sarcasm can be expressed implicitly and require contextual, background, and commonsense knowledge beyond lexical, syntactic, and semantic text analysis.
Method
CASCADE combines CNN-based comment modeling with discourse features and user embeddings encoding users’ stylometric and personality features.
Results
CASCADE achieves state-of-the-art performance on the large-scale SARC Reddit corpus, with significant improvement across datasets over existing detectors.
Takeaways & Limitations
Discourse features and user embeddings play a crucial role in CASCADE’s sarcasm-detection performance.
Takeaways & Limitations
The model can fail when contextual comments are very long or when required commonsense and background knowledge is unavailable; sequential RNN discourse modeling may help with long comments.
Abstract
from arXiv · showhide
The literature in automated sarcasm detection has mainly focused on lexical, syntactic and semantic-level analysis of text. However, a sarcastic sentence can be expressed with contextual presumptions, background and commonsense knowledge. In this paper, we propose CASCADE (a ContextuAl SarCasm DEtector) that adopts a hybrid approach of both content and context-driven modeling for sarcasm detection in online social media discussions. For the latter, CASCADE aims at extracting contextual information from the discourse of a discussion thread. Also, since the sarcastic nature and form of expression can vary from person to person, CASCADE utilizes user embeddings that encode stylometric and personality features of the users. When used along with content-based feature extractors such as Convolutional Neural Networks (CNNs), we see a significant boost in the classification performance on a large Reddit corpus.
1 Introduction
CASCADE addresses the difficulty of detecting implicit, context-dependent sarcasm in online discussions by combining content modeling with user and discourse context. It uses a hybrid architecture that integrates CNN text representations, user embeddings, and discussion-level discourse features.
- Motivation: Implicit sarcasm often requires commonsense, background, and previous-comment context beyond lexical cues, making it difficult to detect in discussion forums.Slang and informal language further reduce the reliability of explicit lexical indicators.
- Proposed approach: CASCADE models sarcasm using both content and contextual information from online discussion forums.The approach combines comment-level text processing with contextual information extracted from users and forum discourse.
- Context modeling: User embeddings capture stylometric and personality features derived from users’ historical posts.These features are fused using Canonical Correlation Analysis to form comprehensive user representations.
- Context modeling: Discussion-level discourse modeling supplies topical, background, and contextual cues from consolidated comments in the same forum.The authors hypothesize that these discourse features provide information needed for sarcasm detection.
- Prediction model: CASCADE concatenates CNN-based syntactic features with relevant user embeddings and discourse features before classification.The resulting unified representation is used to predict whether a comment is sarcastic.
- Evaluation: Experiments on the large SARC Reddit corpus show significant performance improvement over state-of-the-art automated sarcasm detectors.The corpus is designed to investigate contextual information and includes user metadata useful for contextual modeling.
2 Related Work
Prior sarcasm-detection research includes content-based models that identify linguistic cues and context-based models that address the temporal and contextual limitations of online text. CASCADE extends context modeling through stylometric, personality, and previously unexplored discourse features.
- Content-based models: Content-based systems treat sarcasm detection as classification using lexical, pragmatic, prosodic, syntactic, and related cues.Examples include interjections, emoticons, quotation marks, prosodic features, and spectral cues.
- Context-based models: Context-based systems add information for online texts whose grammatical inaccuracies and temporal context make linguistic mining less effective.Prior work shows that traditional classifiers can fail when humans require additional context.
- CASCADE’s distinction: CASCADE learns user embeddings using stylometric and personality descriptions rather than relying only on earlier user-modeling approaches.The paper reports empirical evidence that these proposed features outperform previous user representations.
- CASCADE’s distinction: CASCADE also learns discourse features, which the authors identify as not previously explored for this task.
3.1 Task Definition
The task is to determine whether a Reddit comment posted by a particular user in a discussion forum is sarcastic.
- Task definition: Given a comment Cij by user ui in discussion forum tj, the objective is to predict whether Cij is sarcastic.The formal setting includes Nu users and Nt discussion forums, with each user participating across a subset of forums.
3.2 Summary of the Proposed Approach
CASCADE represents each comment through both its textual content and its surrounding context. It uses a CNN for comment representations and augments them with learned user and discourse features.
- Content modeling: A CNN generates a comment representation by extracting location-invariant local patterns that capture syntactic and semantic information.
- Contextual modeling: CASCADE learns user embeddings and discourse features to provide contextual information for each comment.
3.3 Learning Contextual Features
CASCADE learns contextual features from users and discussion forums to capture information relevant to sarcasm detection.
- User embeddings capture traits that correlate with users’ sarcastic tendencies.They are learned from each user’s accumulated historical posts.
- Discourse features represent contextual information from comments within each discussion forum.These features aim to acquire discriminative information for sarcasm detection.
3.4 User Embeddings
CASCADE builds user embeddings from stylometric and personality features, then fuses the two views with CCA into a single representation.
- CASCADE models each user’s stylometric and personality features before fusing them into one embedding.Historical user comments support stylometric modeling, while personality features are inferred from comments.
- Stylometric features are learned by applying ParagraphVector to a document formed from all comments written by a user.The resulting document vectors represent users’ stylometric features.
- Personality features are inferred with a CNN pretrained for multilabel prediction of the five Big-Five personality traits.The model uses activations from the CNN’s last hidden layer and averages them across a user’s comments.
- The stylometric and personality embedding views are fused with CCA to create a combined user representation.CCA finds projections with maximum correlation and can be extended to more than two views using Generalized CCA.
3.5 Discourse Features
CASCADE models discussion-forum discourse as contextual information that can provide topical and background cues for sarcasm detection.
- Discussion forums may contain topical information that selectively biases the degree of sarcasm in their comments.The paper gives political leaders and sports matches as more sarcasm-susceptible topics than natural disasters.
- Each forum’s comments are appended into a document, and ParagraphVector generates a discourse representation for that forum.The resulting vector represents the forum’s discourse features.
3.6 Final Prediction
CASCADE combines a comment’s CNN-derived representation with its author and forum context vectors before classification.
- CASCADE concatenates the comment representation, relevant user embedding, and forum discourse vector into a unified text representation.The comment representation is extracted with a CNN, and ⊕ denotes concatenation.
- The unified representation is projected to an output layer for predicting the comment’s sarcasm class.The training objective uses one-hot ground truth labels and predicted class probabilities.
4 Experimental Results
Experiments on SARC evaluate CASCADE across balanced, imbalanced, and politics-focused Reddit datasets, comparing it with baselines and analyzing its contextual components. CASCADE achieves the strongest reported performance, with user embeddings and discourse features contributing substantially to the results.
- Dataset: SARC contains more than a million Reddit sarcastic and non-sarcastic statements organized in topic-specific, tree-structured discussion forums.Comments include author details and parent comments, enabling discourse-based contextual modeling.
- Dataset: The experiments use balanced Main, approximately 20:80 imbalanced Main, and politics-focused Pol dataset variants.The Pol subset tests user embeddings on forums associated with politics, while the imbalanced set emulates real-world class distributions.
- Overall results: CASCADE significantly improves performance across all SARC datasets and outperforms Bag-of-Words, CNN, CNN-SVM, and CUE-CNN.The reported comparisons use 10 runs with different initializations and assert significance at p < 0.05 under a paired-t test.
- User-profiling analysis: Removing personality features reduces CASCADE performance to a range similar to CUE-CNN, indicating that stylometric and personality features jointly support its gains.The comparison evaluates CASCADE’s user-profiling method against CUE-CNN’s ParagraphVector-like user embeddings.
- Ablation study: Adding user embeddings produces an 8−12% accuracy and F1 boost, while discourse features increase F1 by 3% on the Pol dataset.The content-only CNN has almost 10% lower accuracy than the optimal configuration, and the full model performs best on all three datasets.
- Ablation study: CCA-based user embeddings outperform simple concatenation, whose increased parameterization is assumed to contribute to a significant performance drop.Using discourse features with concatenated embeddings does not recover the improvement.
5 Conclusion
CASCADE combines content modeling with user profiling and discourse modeling to detect sarcasm, achieving state-of-the-art performance on a large-scale Reddit corpus. The results indicate that discourse features and user embeddings are important to system performance.
- CASCADE combines content and contextual information for sarcasm classification.
- User profiling and discussion-thread discourse modeling provide the contextual features used alongside a CNN-based textual model.
- CASCADE obtains state-of-the-art performance on a large-scale Reddit corpus, with discourse features and user embeddings playing a crucial role.