Implementation of emotional features on satire detection

Thu, Pyae Phyo; New, Nwe

doi:10.1109/snpd.2017.8022715

Cited by 11 publications

(13 citation statements)

References 14 publications

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“…Usually, satirical posts are seen in indirect speech, wherein sophisticated content is used to convey implicit semantics. Nowadays, satire has been seen extensively over OSM platforms to propagate misinformation [3]. Mostly, satire is used in the form of news (aka satirical news) on the Internet.…”

Section: Satire In Online Social Mediamentioning

confidence: 99%

“…They considered psycholinguistic features and considered seven groups of classifiers (Bayesian, functions, lazy, metaclassifiers, rules, miscellaneous, and trees) to perform the classification task. Thu and New [3] highlighted emotional features for satire detection from three online sources and applied SVM and bagging classifiers.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, a crawler in Python 2.7 is implemented to crawl tweets through a popular Python library called tweepy. 3 It fetches tweets against its tweet ids by accessing the Twitter REST API. Further, these tweets are stored in a local repository.…”

Section: Data Crawling and Pre-processingmentioning

confidence: 99%

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Contextualized Satire Detection in Short Texts Using Deep Learning Techniques

Kamal,

Abulaish,

Jahiruddin

2024

JWE

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Satire is prominent in user-generated content on various online platforms in the form of satirical news, customer reviews, blogs, articles, and short messages that are typically of an informal nature. As satire is also used to disseminate false information on the Internet, its computational detection has become a well-known issue. Existing work focuses primarily on formal document- or sentence-level textual data, whereas informal short texts have gotten less attention for satire detection. This paper presents a new model called BiLSTM self-attention (BiSAT) for detecting satire in informal short texts. It consists of various components such as input, embedding, self-attention, and two bi-directional long short-term memory (BiLSTM) layers for learning crucial contextual information pertaining to the satire present in the texts. The input layer uses the text as input to create an input vector, which is then given to the embedding layer to create the appropriate numeric vector. The output of the embedding layer is passed on to the first BiLSTM layer, which extracts contextual information-based sequences in the opposite direction. Between the first and second BiLSTM layers, a self-attention layer is employed to draw attention to the important satirical information that is acquired by the hidden layer of the first BiLSTM. The BiSAT model also takes a classic feature engineering approach, employing a 13-dimensional auxiliary feature vector comprised of features from four separate feature categories: sentiment, punctuation, hyperbole, and affective. The proposed BiSAT model is empirically evaluated on two benchmark datasets and a newly created dataset called Satire-280. It outperforms existing research and baseline methods by a significant margin. The Satire-280 dataset along with code can be downloaded from GitHub repository: https://github.com/Ashraf-Kamal/Satire-Detection.

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Section: Satire In Online Social Mediamentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Contextualized Satire Detection in Short Texts Using Deep Learning Techniques

Kamal,

Abulaish,

Jahiruddin

2024

JWE

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“…Recently, researchers have begun to look at it from a different computational perspective. For instance, authors of [6] adopt an emotion-based approach and those of [7] proposed a method based on stylometry. Only a few authors adopt linguistic-based approaches to accomplish the satire detection task.…”

Section: Related Workmentioning

confidence: 99%

“…Moreover, Yang, Mukherjee, and Gragut [10] identified four main categories of features able to guarantee an acceptable characterization of satiric content: Writing-Stylistic, Readability, Structural, and Psycho-linguistic. Several works, like [6,8,11,12], leverage on Doc2Vect or Term Frequency Inverse Document Frequency (TF-IDF) to construct features able to train classifiers for satires. Such approaches, on one side, provide good performance but, on the other side, fail to characterize satire in a human-understandable and explainable way.…”

Section: Related Workmentioning

confidence: 99%

Satiric Content Detection Through Linguistic Features

Gaeta,

Orciuoli,

Pascuzzo

2023

Machine Learning and Artificial Intelligence

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In the age of Information Disorder, Satire is one of its phenomena mainly occurring in the context of social media. Satire represents an interesting study subject given that it can be easily confused with further forms of the disorder. The present work proposes and evaluates a set of linguistic features to build classifiers able to distinguish satires from other textual contents. The adopted features are firstly identified within the scientific literature and, secondly, ranked and filtered by means of the Information Gain index. Several experimentation activities show good performance for the aforementioned classifiers and an acceptable ability to generalize for the models trained with such features.

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