sarcasm detection and quantification in arabic tweets

• URL: http://arxiv.org/abs/2108.01425v1
• Date: Tue, 3 Aug 2021 11:48:27 GMT
• Title: sarcasm detection and quantification in arabic tweets
• Authors: Bashar Talafha, Muhy Eddin Za'ter, Samer Suleiman, Mahmoud Al-Ayyoub, Mohammed N. Al-Kabi
• Abstract summary: This paper intends to create a new humanly annotated Arabic corpus for sarcasm detection collected from tweets. The proposed approach tackles the problem as a regression problem instead of classification.
• Score: 7.173484352846755
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The role of predicting sarcasm in the text is known as automatic sarcasm detection. Given the prevalence and challenges of sarcasm in sentiment-bearing text, this is a critical phase in most sentiment analysis tasks. With the increasing popularity and usage of different social media platforms among users around the world, people are using sarcasm more and more in their day-to-day conversations, social media posts and tweets, and it is considered as a way for people to express their sentiment about some certain topics or issues. As a result of the increasing popularity, researchers started to focus their research endeavors on detecting sarcasm from a text in different languages especially the English language. However, the task of sarcasm detection is a challenging task due to the nature of sarcastic texts; which can be relative and significantly differs from one person to another depending on the topic, region, the user's mentality and other factors. In addition to these challenges, sarcasm detection in the Arabic language has its own challenges due to the complexity of the Arabic language, such as being morphologically rich, with many dialects that significantly vary between each other, while also being lowly resourced. In recent years, only few research attempts started tackling the task of sarcasm detection in Arabic, including creating and collecting corpora, organizing workshops and establishing baseline models. This paper intends to create a new humanly annotated Arabic corpus for sarcasm detection collected from tweets, and implementing a new approach for sarcasm detection and quantification in Arabic tweets. The annotation technique followed in this paper is unique in sarcasm detection and the proposed approach tackles the problem as a regression problem instead of classification; i.e., the model attempts to predict the level of sarcasm instead of binary classification.

Related papers

• 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)
• 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 in a Disaster Context [103.93691731605163]
  We introduce HurricaneSARC, a dataset of 15,000 tweets annotated for intended sarcasm. Our best model is able to obtain as much as 0.70 F1 on our dataset.
  arXiv  Detail & Related papers  (2023-08-16T05:58:12Z)
• 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)
• Interpretable Bangla Sarcasm Detection using BERT and Explainable AI [0.3914676152740142]
  A BERT-based system can achieve 99.60% while the utilized traditional machine learning algorithms are only capable of achieving 89.93%. This dataset consists of fresh records of sarcastic and non-sarcastic comments, the majority of which are acquired from Facebook and YouTube comment sections.
  arXiv  Detail & Related papers  (2023-03-22T17:35:35Z)
• 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)
• 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)
• Forensic Authorship Analysis of Microblogging Texts Using N-Grams and Stylometric Features [63.48764893706088]
  This work aims at identifying authors of tweet messages, which are limited to 280 characters. We use for our experiments a self-captured database of 40 users, with 120 to 200 tweets per user. Results using this small set are promising, with the different features providing a classification accuracy between 92% and 98.5%.
  arXiv  Detail & Related papers  (2020-03-24T19:32:11Z)

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.