Modeling Satire in English Text for Automatic Detection

Reganti, Aishwarya N.; Maheshwari, Tushar; Kumar, Uttam; Das, Amitava; Bajpai, Ram

doi:10.1109/icdmw.2016.0141

Cited by 21 publications

(18 citation statements)

References 36 publications

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“…From literature, Buschmeier et al [18] and Reganti et al, [30] describe irony/satire detection on product review dataset [20] using SVM, LR, DT and RF classifiers. The similar set of experiment is conducted and compared the results.…”

Section: Comparison With Existing Methodsmentioning

confidence: 99%

“…The similar set of experiment is conducted and compared the results. The Table V elucidates the comparisons of the proposed method with [18] and [30] method. In [18], models are evaluated based on precision, recall and F-measure using linear SVM, LR, DT and RF classifiers.…”

Section: Comparison With Existing Methodsmentioning

confidence: 99%

“…Random Forest operates by constructing a multitude of decision trees at training time and outputting the class based on decision of individual trees [35]. These four classifiers are widely used in classification of ironic content in reviews [18] [30].…”

Section: F Ensemble Classifiersmentioning

confidence: 99%

“…In this work, we conducted an experiment based on 80 training and 20 testing splits on product review dataset [20]. The experiments are conducted similarly to [18], [30] to give comparison with existing methods. The experiments are conducted based on two baselines: Feature Subset Selection (FSS) that accomplishes two stages feature selection and Feature Fusion baseline that exploits feature fusion of linguistic features and FSS.…”

Section: B Experimental Setupmentioning

confidence: 99%

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Automatic Irony Detection using Feature Fusion and Ensemble Classifier

Kumar¹,

Harish²

2019

IJIMAI

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With the advent of micro-blogging sites, users are pioneer in expressing their sentiments and emotions on global issues through text. Automatic detection and classification of sentiments like sarcastic or ironic content in microblogging reviews is a challenging task. It requires a system that manages some kind of knowledge to interpret the sentiment expressed in text. The available approaches are quite limited in their capabilities and scope to detect ironic utterances present in the text. In this regards, the paper propose feature fusion to provide knowledge to the system by alternative sets of features obtained using linguistic and content based text features. The proposed work extracts five sets of linguistic features and fuses with features selected using two stages of a feature selection method. In order to demonstrate the effectiveness of the proposed method, we conduct extensive experimentation by selecting different feature subsets. The performances of the proposed method are evaluated using Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), Decision Tree (DT) and ensemble classifiers. The experimental result shows the proposed approach significantly out-performs the conventional methods.

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Section: Comparison With Existing Methodsmentioning

confidence: 99%

Section: Comparison With Existing Methodsmentioning

confidence: 99%

Section: F Ensemble Classifiersmentioning

confidence: 99%

Section: B Experimental Setupmentioning

confidence: 99%

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Automatic Irony Detection using Feature Fusion and Ensemble Classifier

Kumar¹,

Harish²

2019

IJIMAI

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“…Some works have also been carried out for detecting satire in English text, for example [10]. Firstly, authors introduce approach to binary classification of satire in English text.…”

Section: Related Workmentioning

confidence: 99%

Recognition of Ironic Sentences in Twitter using Attention-Based LSTM

Martini¹,

Farrukh²,

Ge³

2018

ijacsa

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Abstract-Analyzing written language is an interesting topic that has been studied by many disciplines. Recently, due to the explosive growth of Internet, social media has become an attractive source of searching and getting information for research purposes on written communication. It is true that different words in a sentence serve different purposes of conveying the meaning while they are of different significance. Therefore, this paper is going to employ the attention mechanism to find out the relative contribution or significance of every word in the sentence. In this work, we address the problem of detecting whether a tweet is ironic or not by using Attention-Based Long Short-Term Memory Network. The results show that the proposed method achieves competitive performance on average recall and F1 score compared to the state-of-the-art results.

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