Multi-Modal Sarcasm Detection in Social Networks: A Comparative Review

Dutta, Poulami; Bhattacharyya, Chandan Kumar

doi:10.1109/iccmc53470.2022.9753981

Cited by 7 publications

(2 citation statements)

References 16 publications

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“…Around thousand such references can be drawn on either of the two problems-aspect based categorisation of online data and language specific algorithm to apply with sentiment analysis using aspectbased methods. In [8], Dutta et al presented a review of different methods of sarcasm detection done so far. From which we can get a clear knowledge of methods invented so far for detecting good or bad words.…”

Section: Literature Reviewmentioning

confidence: 99%

Early detection of cyber-bullying from twitter data

Das¹,

Sengupta

Das³

2022

ijhs

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The vast volume of user created data in social media has created a wide-open path to data analytics in terms of extraction of features, analysis of polarity of information and classification of human emotional state. Sentiment analysis and data mining has reached at its peak in terms of its use in feature extraction and commercial analysis of products. Enormous approaches have been also seen in human behaviour analysis using ML (Machine Learning) algorithms and NLP (Natural Language Processing). To be very specific, it is not too hard to analyse data in granular level such as aspects. Social media play a distinct role in providing large dataset at no cost. And aspect-based sentiment analysis provides us the text analysis technique that categorisesuser provided data and assigns positive or negative value for the sentiment identified in it. In our approach we have tried to find out a solution of detection of cyber bullying in human interaction. This problem, we find a new threat in this digital era as commenting and replying to that comment play an important role in virtual communication, and just in this point, it takes no cost or hesitation factor to attack another person verbally or virtually.

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Section: Literature Reviewmentioning

confidence: 99%

Early detection of cyber-bullying from twitter data

Das¹,

Sengupta

Das³

2022

ijhs

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“…However, identifying sarcasm is challenging because none of these indicators is readily present in written communication. Identifying sarcastic comments for images, videos, or text shared over social platforms is even more difficult as context lies with the image or the main text/comment/headline [3,4]. Sarcasm identification in online communications from social media sites, discussion forums, and e-commerce websites has become essential for fake news detection, sentiment analysis, opinion mining, and detecting of online trolls and cyberbullies [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Sarcasm Detection over Social Media Platforms Using Hybrid Auto-Encoder-Based Model

et al. 2022

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Sarcasm is a language phrase that conveys the polar opposite of what is being said, generally something highly unpleasant to offend or mock somebody. Sarcasm is widely used on social media platforms every day. Because sarcasm may change the meaning of a statement, the opinion analysis procedure is prone to errors. Concerns about the integrity of analytics have grown as the usage of automated social media analysis tools has expanded. According to preliminary research, sarcastic statements alone have significantly reduced the accuracy of automatic sentiment analysis. Sarcastic phrases also impact automatic fake news detection leading to false positives. Various individual natural language processing techniques have been proposed earlier, but each has textual context and proximity limitations. They cannot handle diverse content types. In this research paper, we propose a novel hybrid sentence embedding-based technique using an autoencoder. The framework proposes using sentence embedding from long short term memory-autoencoder, bidirectional encoder representation transformer, and universal sentence encoder. The text over images is also considered to handle multimedia content such as images and videos. The final framework is designed after the ablation study of various hybrid fusions of models. The proposed model is verified on three diverse real-world social media datasets—Self-Annotated Reddit Corpus (SARC), headlines dataset, and Twitter dataset. The accuracy of 83.92%, 90.8%, and 92.80% is achieved. The accuracy metric values are better than previous state-of-art frameworks.

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Image-Text Sarcasm Detection for Enhanced Understanding

2024

Lecture Notes in Computer Science

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Multi-Modal Sarcasm Detection in Social Networks: A Comparative Review

Cited by 7 publications

References 16 publications

Early detection of cyber-bullying from twitter data

Early detection of cyber-bullying from twitter data

Sarcasm Detection over Social Media Platforms Using Hybrid Auto-Encoder-Based Model

Image-Text Sarcasm Detection for Enhanced Understanding

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