A Multi-Dimension Question Answering Network for Sarcasm Detection

Diao, Yuanan; Lin, Hongfei; Yang, Liang; Fan, Xiaochao; Chu, Yonghe; Wu, Di

doi:10.1109/access.2020.2967095

Cited by 23 publications

(7 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A Twitter dataset consisting of 107536 tweets was employed to compute and compare the formulated model against the existing technique for detecting the sarcasm. A new MAQ (multidimension question answering) network was recommended by Diao et al for detecting the sarcasm [ 25 ]. This technique was efficient to provide the plentiful semantic information for analyzing the ambiguity of sarcasm with the help of multidimension representations.…”

Section: Literature Reviewmentioning

confidence: 99%

Ensemble Classification Approach for Sarcasm Detection

Godara

Batra

Aron

et al. 2021

Behavioural Neurology

View full text Add to dashboard Cite

Cognitive science is a technology which focuses on analyzing the human brain using the application of DM. The databases are utilized to gather and store the large volume of data. The authenticated information is extracted using measures. This research work is based on detecting the sarcasm from the text data. This research work introduces a scheme to detect sarcasm based on PCA algorithm, K -means algorithm, and ensemble classification. The four ensemble classifiers are designed with the objective of detecting the sarcasm. The first ensemble classification algorithm (SKD) is the combination of SVM, KNN, and decision tree. In the second ensemble classifier (SLD), SVM, logistic regression, and decision tree classifiers are combined for the sarcasm detection. In the third ensemble model (MLD), MLP, logistic regression, and decision tree are combined, and the last one (SLM) is the combination of MLP, logistic regression, and SVM. The proposed model is implemented in Python and tested on five datasets of different sizes. The performance of the models is tested with regard to various metrics.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Ensemble Classification Approach for Sarcasm Detection

Godara

Batra

Aron

et al. 2021

Behavioural Neurology

View full text Add to dashboard Cite

show abstract

“…They used SVM for handcrafted feature extraction to be used as input for the proposed model. Another work in [43] utilized an attention-based Bi-LSTM for sarcasm classification. For better word embedding, a question answering network was designed based on five different layers, each of which provides different representations.…”

Section: Dl-based Approachesmentioning

confidence: 99%

Text-based Sarcasm Detection on Social Networks: A Systematic Review

Alqahtani¹,

Alhenaki²,

Alsheddi³

2023

IJACSA

View full text Add to dashboard Cite

Sarcasm is a sophisticated phenomenon used for conveying a meaning that differs from what is being said, and it is usually used to express displeasure or ridicule others. Sentiment analysis is a process of uncovering the subjective information from a text. Detecting figurative language such as irony or sarcasm, is a focused challenging research field of sentiment analysis. Detecting and understanding the use of sarcasm in social networks could provide businesses and politicians with significant insight, since it reflects people's opinions about certain topics, news, and products. This has especially become relevant recently because sarcastic texts have been trending on social networks and are being posted by millions of active users. As a result of this situation, there is now an increasing amount of research on the detection of sarcasm in social network posts. Many works have been published on sarcasm detection, and they include a wide variety of techniques based on rules, lexicons, traditional machine learning, deep learning, and transformers. However, sarcasm detection is a challenging task due to the ambiguity and non-straightforward nature of sarcastic text. In addition, very few reviews have been conducted on the research in this area. Therefore, this systematic review mainly aims at exploring the newly published sarcasm detection articles on social networks in the years between 2019 and 2022. Several databases were extensively searched, and 30 articles that met the criteria were included. The selected articles were reviewed based on their approaches, datasets, and evaluation metrics. The findings emphasized that deep learning is the most commonly used technique for sarcasm detection in recent literature, and Twitter and F-measure are the most used source and performance metric, respectively. Finally, this article presents a brief discussion regarding the challenges in sarcasm detection and future research directions.

show abstract

“…The output of LSTM layers fed to a Fully Connected Neural Network in order to produce a higher-order feature set. Diao et al (2020) proposed a novel multi-dimension question answering network in order to detect sarcasm. They utilized conversation context information.…”

Section: Related Workmentioning

confidence: 99%

Applying Transformers and Aspect-based Sentiment Analysis approaches on Sarcasm Detection

ataei¹,

Javdan²,

Minaei‐Bidgoli

2020

Proceedings of the Second Workshop on Figurative Language Processing

View full text Add to dashboard Cite

Sarcasm is a type of figurative language broadly adopted in social media and daily conversations. The sarcasm can ultimately alter the meaning of the sentence, which makes the opinion analysis process error-prone. In this paper, we propose to employ bidirectional encoder representations transformers (BERT), and aspect-based sentiment analysis approaches in order to extract the relation between context dialogue sequence and response and determine whether or not the response is sarcastic. The best performing method of ours obtains an F1 score of 0.73 on the Twitter dataset and 0.734 over the Reddit dataset at the second workshop on figurative language processing Shared Task 2020.

show abstract

A Multi-Dimension Question Answering Network for Sarcasm Detection

Cited by 23 publications

References 30 publications

Ensemble Classification Approach for Sarcasm Detection

Ensemble Classification Approach for Sarcasm Detection

Text-based Sarcasm Detection on Social Networks: A Systematic Review

Applying Transformers and Aspect-based Sentiment Analysis approaches on Sarcasm Detection

Contact Info

Product

Resources

About