"When Words Fail, Emojis Prevail": Generating Sarcastic Utterances with
Emoji Using Valence Reversal and Semantic Incongruity
- URL: http://arxiv.org/abs/2305.04105v2
- Date: Fri, 16 Jun 2023 15:11:03 GMT
- Title: "When Words Fail, Emojis Prevail": Generating Sarcastic Utterances with
Emoji Using Valence Reversal and Semantic Incongruity
- Authors: Faria Binte Kader, Nafisa Hossain Nujat, Tasmia Binte Sogir, Mohsinul
Kabir, Hasan Mahmud and Kamrul Hasan
- Abstract summary: We present a novel architecture for sarcasm generation with emoji from a non-sarcastic input sentence in English.
We conclude our study by evaluating the generated sarcastic sentences using human judgement.
- Score: 0.23488056916440855
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Sarcasm is a form of figurative language that serves as a humorous tool for
mockery and ridicule. We present a novel architecture for sarcasm generation
with emoji from a non-sarcastic input sentence in English. We divide the
generation task into two sub tasks: one for generating textual sarcasm and
another for collecting emojis associated with those sarcastic sentences. Two
key elements of sarcasm are incorporated into the textual sarcasm generation
task: valence reversal and semantic incongruity with context, where the context
may involve shared commonsense or general knowledge between the speaker and
their audience. The majority of existing sarcasm generation works have focused
on this textual form. However, in the real world, when written texts fall short
of effectively capturing the emotional cues of spoken and face-to-face
communication, people often opt for emojis to accurately express their
emotions. Due to the wide range of applications of emojis, incorporating
appropriate emojis to generate textual sarcastic sentences helps advance
sarcasm generation. We conclude our study by evaluating the generated sarcastic
sentences using human judgement. All the codes and data used in this study has
been made publicly available.
Related papers
- The Prosody of Emojis [73.70220975424597]
This study examines how emojis influence prosodic realisation in speech and how listeners interpret prosodic cues to recover emoji meanings.<n>Unlike previous work, we directly link prosody and emoji by analysing actual human speech data, collected through structured but open-ended production and perception tasks.<n>Results show that speakers adapt their prosody based on emoji cues, listeners can often identify the intended emoji from prosodic variation alone, and greater semantic differences between emojis correspond to increased prosodic divergence.
arXiv Detail & Related papers (2025-08-01T11:24:12Z) - Sarc7: Evaluating Sarcasm Detection and Generation with Seven Types and Emotion-Informed Techniques [4.699432725785436]
Sarcasm is a form of humor where expressions convey meanings opposite to their literal interpretations.<n>We introduce Sarc7, a benchmark that classifies 7 types of sarcasm: self-deprecating, brooding, deadpan, polite, obnoxious, raging, and manic.<n>We propose an emotion-based generation method developed by identifying key components of sarcasm-incongruity, shock value, and context dependency.
arXiv Detail & Related papers (2025-05-31T18:01:23Z) - Irony in Emojis: A Comparative Study of Human and LLM Interpretation [53.66354612549173]
This study examines the ability of GPT-4o to interpret irony in emojis.
By prompting GPT-4o to evaluate the likelihood of specific emojis being used to express irony on social media, we aim to bridge the gap between machine and human understanding.
arXiv Detail & Related papers (2025-01-20T03:02:00Z) - Was that Sarcasm?: A Literature Survey on Sarcasm Detection [0.19736111241221438]
Being able to interpret sarcasm is often termed as a sign of intelligence, given the complex nature of sarcasm.
This Literature Survey delves into different aspects of sarcasm detection, to create an understanding of the underlying problems faced during detection, approaches used to solve this problem, and different forms of available datasets for sarcasm detection.
arXiv Detail & Related papers (2024-11-30T10:38:26Z) - Semantics Preserving Emoji Recommendation with Large Language Models [47.94761630160614]
Existing emoji recommendation methods are primarily evaluated based on their ability to match the exact emoji a user chooses in the original text.
We propose a new semantics preserving evaluation framework for emoji recommendation, which measures a model's ability to recommend emojis that maintain the semantic consistency with the user's text.
arXiv Detail & Related papers (2024-09-16T22:27:46Z) - Impact of emoji exclusion on the performance of Arabic sarcasm detection models [0.0]
In this paper, we investigate the impact of a fundamental preprocessing component on sarcasm speech detection.
We use AraBERT pre-training to refine the specified models, demonstrating that the removal of emojis can significantly boost the accuracy of sarcasm detection.
This study establishes new benchmarks in Arabic natural language processing and presents valuable insights for social media platforms.
arXiv Detail & Related papers (2024-05-03T15:51:02Z) - EmojiLM: Modeling the New Emoji Language [44.23076273155259]
We develop a text-emoji parallel corpus, Text2Emoji, from a large language model.
Based on the parallel corpus, we distill a sequence-to-sequence model, EmojiLM, which is specialized in the text-emoji bidirectional translation.
Our proposed model outperforms strong baselines and the parallel corpus benefits emoji-related downstream tasks.
arXiv Detail & Related papers (2023-11-03T07:06:51Z) - Researchers eye-view of sarcasm detection in social media textual
content [0.0]
Enormous use of sarcastic text in all forms of communication in social media will have a physiological effect on target users.
This paper discusses various sarcasm detection techniques and concludes with some approaches, related datasets with optimal features.
arXiv Detail & Related papers (2023-04-17T19:45:10Z) - 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) - On the Context-Free Ambiguity of Emoji: A Data-Driven Study of 1,289
Emojis [28.04805745702487]
We collect a crowdsourced dataset of one-word emoji descriptions for 1,289 emojis presented to participants with no surrounding text.
We find that with 30 annotations per emoji, 16 emojis are completely unambiguous, whereas 55 emojis are so ambiguous that their descriptions are indistinguishable from randomly chosen descriptions.
arXiv Detail & Related papers (2022-01-17T09:33:29Z) - 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) - Semantic Journeys: Quantifying Change in Emoji Meaning from 2012-2018 [66.28665205489845]
We offer the first longitudinal study of how emoji semantics changes over time, applying techniques from computational linguistics to six years of Twitter data.
We identify five patterns in emoji semantic development and find evidence that the less abstract an emoji is, the more likely it is to undergo semantic change.
To aid future work on emoji and semantics, we make our data publicly available along with a web-based interface that anyone can use to explore semantic change in emoji.
arXiv Detail & Related papers (2021-05-03T13:35:10Z) - $R^3$: 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.