AI-Based Learning Techniques for Sarcasm Detection of Social Media Tweets: State-of-the-Art Survey

Kumar, Yogesh; Goel, Nikita

doi:10.1007/s42979-020-00336-3

Cited by 13 publications

(10 citation statements)

References 12 publications

(9 reference statements)

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“…The SA method is used in many fields, especially in social media [ 57 ], such as classifying users’ opinions on social media posts [ 58 , 59 ], or knowing the tendencies of the masses in elections and predicting the final results, in addition to controlling public opinion by understanding the public’s attitudes by analyzing users’ opinions about certain situations [ 32 ]. Additionally, it contributes to commercial marketing by exploring the desires of consumers towards the goods offered on social media platforms [ 60 , 61 , 62 ]. SA is also used to detect fake news, and it is an influential factor in determining misleading information [ 63 ], by presenting crucial information about its content.…”

Section: Background Of Studymentioning

confidence: 99%

Fake News Detection Model on Social Media by Leveraging Sentiment Analysis of News Content and Emotion Analysis of Users’ Comments

Hamed

Aziz

Yaakub

2023

Sensors

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Nowadays, social media has become the main source of news around the world. The spread of fake news on social networks has become a serious global issue, damaging many aspects, such as political, economic, and social aspects, and negatively affecting the lives of citizens. Fake news often carries negative sentiments, and the public’s response to it carries the emotions of surprise, fear, and disgust. In this article, we extracted features based on sentiment analysis of news articles and emotion analysis of users’ comments regarding this news. These features were fed, along with the content feature of the news, to the proposed bidirectional long short-term memory model to detect fake news. We used the standard Fakeddit dataset that contains news titles and comments posted regarding them to train and test the proposed model. The suggested model, using extracted features, provided a high detection accuracy of 96.77% of the Area under the ROC Curve measure, which is higher than what other state-of-the-art studies offer. The results prove that the features extracted based on sentiment analysis of news, which represents the publisher’s stance, and emotion analysis of comments, which represent the crowd’s stance, contribute to raising the efficiency of the detection model.

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Section: Background Of Studymentioning

confidence: 99%

Fake News Detection Model on Social Media by Leveraging Sentiment Analysis of News Content and Emotion Analysis of Users’ Comments

Hamed

Aziz

Yaakub

2023

Sensors

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“…The easy-to-use network structure lends it to being frequently applied in multiple reasoning systems [7], [1], [37]. Other relevant areas in AI that could potentially benefit from this work include graph-based models [10], meta-heuristics [40], sarcasm detection [30].…”

Section: Related Workmentioning

confidence: 99%

Understanding Substructures in Commonsense Relations in ConceptNet

Shi¹,

Kejriwal²

2022

Preprint

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Acquiring commonsense knowledge and reasoning is an important goal in modern NLP research. Despite much progress, there is still a lack of understanding (especially at scale) of the nature of commonsense knowledge itself. A potential source of structured commonsense knowledge that could be used to derive insights is ConceptNet. In particular, ConceptNet contains several coarse-grained relations, including 'HasContext', 'FormOf' and 'SymbolOf', which can prove invaluable in understanding broad, but critically important, commonsense notions such as 'context'. In this article, we present a methodology based on unsupervised knowledge graph representation learning and clustering to reveal and study substructures in three heavily used commonsense relations in ConceptNet. Our results show that, despite having an 'official' definition in ConceptNet, many of these commonsense relations exhibit considerable sub-structure. In the future, therefore, such relations could be sub-divided into other relations with more refined definitions. We also supplement our core study with visualizations and qualitative analyses.

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“…Over the past years, a few reviews about sarcasm detection in social networks have been published, but most of them focused mainly on the implementation phase, for example, [5], [6] and [7]. However, some of the previous research did not cover all the approaches used for sarcasm detection.…”

Section: Introductionmentioning

confidence: 99%

“…However, there are many other techniques in use that need to be studied. The researchers in [7] reviewed the rule-based, statistical-based, and deep learning (DL) approaches for sarcasm detection but did not consider other popular approaches such as transformers, while the researchers in [6] only presented a technical review of sarcasm detection algorithms and reported the mostly frequently used algorithms for sarcasm identification.…”

Section: Introductionmentioning

confidence: 99%

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

Alqahtani¹,

Alhenaki²,

Alsheddi³

2023

IJACSA

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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.

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AI-Based Learning Techniques for Sarcasm Detection of Social Media Tweets: State-of-the-Art Survey

Cited by 13 publications

References 12 publications

Fake News Detection Model on Social Media by Leveraging Sentiment Analysis of News Content and Emotion Analysis of Users’ Comments

Fake News Detection Model on Social Media by Leveraging Sentiment Analysis of News Content and Emotion Analysis of Users’ Comments

Understanding Substructures in Commonsense Relations in ConceptNet

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

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