A survey on approaches and issues for detecting sarcasm on social media tweets

Kanakam, Ranganath; Mohmmad, Sallauddin; Sudarshan, E. C. G.; Shabana, N.A.; Gopal, Maisagalla

doi:10.1063/5.0082034

Cited by 5 publications

(3 citation statements)

References 29 publications

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“…The efficiency rate of the classifier is higher than that of other supervised learning methods. In this method, each attribute pair operates independently of the others (Kanakam et al, 2022). • Proposed Model: The proposed approach is a hybrid one that uses two vectorization models to provide a solution to the sarcasm detection problem.…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

Sarcasm Text Detection on News Headlines Using Novel Hybrid Machine Learning Techniques

Singh

2024

ADCAIJ

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One of the biggest problems with sentiment analysis systems is sarcasm. The use of implicit, indirect language to express opinions is what gives it its complexity. Sarcasm can be represented in a number of ways, such as in headings, conversations, or book titles. Even for a human, recognizing sarcasm can be difficult because it conveys feelings that are diametrically contrary to the literal meaning expressed in the text. There are several different models for sarcasm detection. To identify humorous news headlines, this article assessed vectorization algorithms and several machine learning models. The recommended hybrid technique using the bag-of-words and TF-IDF feature vectorization models is compared experimentally to other machine learning approaches. In comparison to existing strategies, experiments demonstrate that the proposed hybrid technique with the bag-of-word vectorization model offers greater accuracy and F1-score results.

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Section: Machine Learning Techniquesmentioning

confidence: 99%

Sarcasm Text Detection on News Headlines Using Novel Hybrid Machine Learning Techniques

Singh

2024

ADCAIJ

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“…In this study, Naïve Bayes algorithm is used for the classification of large twitter data. Naïve Bayes [21]Classifier is a scalable method that used a lot of parameters for Classification purposes. It is a supervised learning model that classified the attributes (features) in the form of binary or multi-class.…”

Section: Naïve Bayesmentioning

confidence: 99%

Classification of Large Social Twitter Network Data Using R

Umer

Iqbal

Mansoor

et al. 2023

JICET

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The development of social networks has altered computer science research. Now, a vast amount of data is available via Twitter, Facebook, emails, and IoT. (Internet of Things). So, storing and analyzing these data has become very difficult for academics. Conventional frameworks have been ineffective in processing massive amounts of data. R is an open-source programming language designed for large-scale data analysis with higher accuracy. Additionally, it offers the chance to implement the R programming language. This essay examines the application of R to classify sizable social network data. The Naive Bayes method is used to categorize massive amounts of Twitter data. The experiment has demonstrated that a sizable portion of data may be adequately classified with positive outcomes utilizing the R framework.

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“…For example, 'Its all going way beyong Funny, Enjoy', can look convincingly like a happy emotion tweet for a trained model, but humans cannot miss the sarcastic undertone. Sarcasm detection has a dedicated field of active research [8] [9]. Moreover, the misspelled word "beyong" cannot be easily spotted and corrected, which is a simple human task.…”

Section: Introductionmentioning

confidence: 99%

MDFBEAM: Multi-Dimensional Fuzzy-Based Emotion Analysis Framework for Micro-Blog News Sub-Events

Vijayakumar,

2023

Preprint

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Social media has replaced traditional journalism, such as television and newspapers, by a large proportion. Social media data presents an incredible opportunity for governments and organizations to understand the public's response to a news event, which is critical for government organizations to make informed decisions. News events are made up of a series of related sub-events. These sub-events determine the flow and dynamics of the entire news event. Hence, monitoring the public's response to each sub-event is crucial. This work proposes a framework to classify microblogs that discuss news sub-events into eight dimensions of emotion: Happy, Sad, Fear, Anger, Cynical, Neutral, Positive and Negative. This work introduces a cynical emotion that attempts to catch skepticism and sarcastic emotions, which are highly common in news-based events. Not limited to news events and microblogs, this framework can be utilized on any public response monitoring system without much changes. The proposed framework employs a bi-layered composite fuzzy mechanism. The emotions are represented as fuzzy sets. The fuzzy membership score of a microblog towards each fuzzy set is generated by integrating neural-network-based pre-trained word vector similarity, thesaurus-based similarity, and linguistic hedge information for each word. Fuzzy inference is then employed to identify the predominant emotion expressed in a microblog. This work is compared against state-of-the-art BERT models and demonstrates a remarkably high level of precision.

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A survey on approaches and issues for detecting sarcasm on social media tweets

Cited by 5 publications

References 29 publications

Sarcasm Text Detection on News Headlines Using Novel Hybrid Machine Learning Techniques

Sarcasm Text Detection on News Headlines Using Novel Hybrid Machine Learning Techniques

Classification of Large Social Twitter Network Data Using R

MDFBEAM: Multi-Dimensional Fuzzy-Based Emotion Analysis Framework for Micro-Blog News Sub-Events

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