A Functional Trade-off between Prosodic and Semantic Cues in Conveying Sarcasm
- URL: http://arxiv.org/abs/2408.14892v1
- Date: Tue, 27 Aug 2024 09:07:37 GMT
- Title: A Functional Trade-off between Prosodic and Semantic Cues in Conveying Sarcasm
- Authors: Zhu Li, Xiyuan Gao, Yuqing Zhang, Shekhar Nayak, Matt Coler,
- Abstract summary: We analyze the prosodic features within utterances and key phrases belonging to three distinct sarcasm categories.
Results show that in phrases where the sarcastic meaning is salient from the semantics, the prosodic cues are less relevant than when the sarcastic meaning is not evident from the semantics.
- Score: 16.351061648741968
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This study investigates the acoustic features of sarcasm and disentangles the interplay between the propensity of an utterance being used sarcastically and the presence of prosodic cues signaling sarcasm. Using a dataset of sarcastic utterances compiled from television shows, we analyze the prosodic features within utterances and key phrases belonging to three distinct sarcasm categories (embedded, propositional, and illocutionary), which vary in the degree of semantic cues present, and compare them to neutral expressions. Results show that in phrases where the sarcastic meaning is salient from the semantics, the prosodic cues are less relevant than when the sarcastic meaning is not evident from the semantics, suggesting a trade-off between prosodic and semantic cues of sarcasm at the phrase level. These findings highlight a lessened reliance on prosodic modulation in semantically dense sarcastic expressions and a nuanced interaction that shapes the communication of sarcastic intent.
Related papers
- Sentiment-enhanced Graph-based Sarcasm Explanation in Dialogue [67.09698638709065]
We propose a novel sEntiment-enhanceD Graph-based multimodal sarcasm Explanation framework, named EDGE.
In particular, we first propose a lexicon-guided utterance sentiment inference module, where a utterance sentiment refinement strategy is devised.
We then develop a module named Joint Cross Attention-based Sentiment Inference (JCA-SI) by extending the multimodal sentiment analysis model JCA to derive the joint sentiment label for each video-audio clip.
arXiv Detail & Related papers (2024-02-06T03:14:46Z) - Sarcasm Detection Framework Using Emotion and Sentiment Features [62.997667081978825]
We propose a model which incorporates emotion and sentiment features to capture the incongruity intrinsic to sarcasm.
Our approach achieved state-of-the-art results on four datasets from social networking platforms and online media.
arXiv Detail & Related papers (2022-11-23T15:14:44Z) - How to Describe Images in a More Funny Way? Towards a Modular Approach
to Cross-Modal Sarcasm Generation [62.89586083449108]
We study a new problem of cross-modal sarcasm generation (CMSG), i.e., generating a sarcastic description for a given image.
CMSG is challenging as models need to satisfy the characteristics of sarcasm, as well as the correlation between different modalities.
We propose an Extraction-Generation-Ranking based Modular method (EGRM) for cross-model sarcasm generation.
arXiv Detail & Related papers (2022-11-20T14:38:24Z) - Towards Multi-Modal Sarcasm Detection via Hierarchical Congruity
Modeling with Knowledge Enhancement [31.97249246223621]
Sarcasm is a linguistic phenomenon indicating a discrepancy between literal meanings and implied intentions.
Most existing techniques only modeled the atomic-level inconsistencies between the text input and its accompanying image.
We propose a novel hierarchical framework for sarcasm detection by exploring both the atomic-level congruity based on multi-head cross attention mechanism and the composition-level congruity based on graph neural networks.
arXiv Detail & Related papers (2022-10-07T12:44:33Z) - Computational Sarcasm Analysis on Social Media: A Systematic Review [0.23488056916440855]
Sarcasm can be defined as saying or writing the opposite of what one truly wants to express, usually to insult, irritate, or amuse someone.
Because of the obscure nature of sarcasm in textual data, detecting it is difficult and of great interest to the sentiment analysis research community.
arXiv Detail & Related papers (2022-09-13T17:20:19Z) - Multi-Modal Sarcasm Detection Based on Contrastive Attention Mechanism [7.194040730138362]
We construct a Contras-tive-Attention-based Sarcasm Detection (ConAttSD) model, which uses an inter-modality contrastive attention mechanism to extract contrastive features for an utterance.
Our experiments on MUStARD, a benchmark multi-modal sarcasm dataset, demonstrate the effectiveness of the proposed ConAttSD model.
arXiv Detail & Related papers (2021-09-30T14:17:51Z) - A Dual-Channel Framework for Sarcasm Recognition by Detecting Sentiment
Conflict [41.08483236878307]
Sarcasm employs ambivalence, where one says something positive but actually means negative, and vice versa.
We show up the essence of sarcastic text is that the literal sentiment is opposite to the deep sentiment.
We propose a Dual-Channel Framework by modeling both literal and deep sentiments to recognize the sentiment conflict.
arXiv Detail & Related papers (2021-09-08T12:33:19Z) - Parallel Deep Learning-Driven Sarcasm Detection from Pop Culture Text
and English Humor Literature [0.76146285961466]
We manually extract the sarcastic word distribution features of a benchmark pop culture sarcasm corpus.
We generate input sequences formed of the weighted vectors from such words.
Our proposed model for detecting sarcasm peaks a training accuracy of 98.95% when trained with the discussed dataset.
arXiv Detail & Related papers (2021-06-10T14:01:07Z) - "The Boating Store Had Its Best Sail Ever": Pronunciation-attentive
Contextualized Pun Recognition [80.59427655743092]
We propose Pronunciation-attentive Contextualized Pun Recognition (PCPR) to perceive human humor.
PCPR derives contextualized representation for each word in a sentence by capturing the association between the surrounding context and its corresponding phonetic symbols.
Results demonstrate that the proposed approach significantly outperforms the state-of-the-art methods in pun detection and location tasks.
arXiv Detail & Related papers (2020-04-29T20:12:20Z) - $R^3$: Reverse, Retrieve, and Rank for Sarcasm Generation with
Commonsense Knowledge [51.70688120849654]
We propose an unsupervised approach for sarcasm generation based on a non-sarcastic input sentence.
Our method employs a retrieve-and-edit framework to instantiate two major characteristics of sarcasm.
arXiv Detail & Related papers (2020-04-28T02:30:09Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.