Polarity based Sarcasm Detection using Semigraph
- URL: http://arxiv.org/abs/2304.01424v1
- Date: Tue, 4 Apr 2023 00:13:55 GMT
- Title: Polarity based Sarcasm Detection using Semigraph
- Authors: Swapnil Mane and Vaibhav Khatavkar
- Abstract summary: This article presents the inventive method of the semigraph, including semigraph construction and sarcasm detection processes.
A variation of the semigraph is suggested in the pattern-relatedness of the text document.
The proposed method is to obtain the sarcastic and non-sarcastic polarity scores of a document using a semigraph.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Sarcasm is an advanced linguistic expression often found on various online
platforms. Sarcasm detection is challenging in natural language processing
tasks that affect sentiment analysis. This article presents the inventive
method of the semigraph, including semigraph construction and sarcasm detection
processes. A variation of the semigraph is suggested in the pattern-relatedness
of the text document. The proposed method is to obtain the sarcastic and
non-sarcastic polarity scores of a document using a semigraph. The sarcastic
polarity score represents the possibility that a document will become
sarcastic. Sarcasm is detected based on the polarity scoring model. The
performance of the proposed model enhances the existing prior art approach to
sarcasm detection. In the Amazon product review, the model achieved the
accuracy, recall, and f-measure of 0.87, 0.79, and 0.83, respectively.
Related papers
- An Evaluation of State-of-the-Art Large Language Models for Sarcasm
Detection [0.0]
Sarcasm is the use of words by someone who means the opposite of what he is trying to say.
Recent innovations in NLP have provided more possibilities for detecting sarcasm.
arXiv Detail & Related papers (2023-10-07T14:45:43Z) - Sampling and Ranking for Digital Ink Generation on a tight computational
budget [69.15275423815461]
We study ways to maximize the quality of the output of a trained digital ink generative model.
We use and compare the effect of multiple sampling and ranking techniques, in the first ablation study of its kind in the digital ink domain.
arXiv Detail & Related papers (2023-06-02T09:55:15Z) - Watermarking Text Generated by Black-Box Language Models [103.52541557216766]
A watermark-based method was proposed for white-box LLMs, allowing them to embed watermarks during text generation.
A detection algorithm aware of the list can identify the watermarked text.
We develop a watermarking framework for black-box language model usage scenarios.
arXiv Detail & Related papers (2023-05-14T07:37:33Z) - A Watermark for Large Language Models [84.95327142027183]
We propose a watermarking framework for proprietary language models.
The watermark can be embedded with negligible impact on text quality.
It can be detected using an efficient open-source algorithm without access to the language model API or parameters.
arXiv Detail & Related papers (2023-01-24T18:52:59Z) - 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) - 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) - $R^3$: 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.