A novel automatic satire and irony detection using ensembled feature selection and data mining

Ravi, Kumar; Ravi, Vadlamani

doi:10.1016/j.knosys.2016.12.018

Cited by 53 publications

(39 citation statements)

References 37 publications

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“…Literature Survey on Feature Extraction. Ravi and Ravi [4] came up with an ensemble text feature selection method in order to identify sarcasm and irony from reviews and news articles. An AUC value of 91.46% for satiric news and AUC of 88.86% for ironic reviews were recorded.…”

Section: Related Workmentioning

confidence: 99%

Multi-Rule Based Ensemble Feature Selection Model for Sarcasm Type Detection in Twitter

Sundararajan

Palanisamy

2020

Computational Intelligence and Neuroscience

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Sentimental analysis aims at inferring how people express their opinion over any piece of text or topic of interest. This article deals with detection of an implicit form of the sentiment, referred to as sarcasm. Sarcasm conveys the opposite of what people try to convey in order to criticize or ridicule in a humorous way. It plays a vital role in social networks since most of the tweets or posts contain sarcastic nuances. Existing approaches towards the study of sarcasm deals only with the detection of sarcasm. In this paper, in addition to detecting sarcasm from text, an approach has been proposed to identify the type of sarcasm. The main motivation behind determining the types of sarcasm is to identify the level of hurt or the true intent behind the sarcastic text. The proposed work aims to improve upon the existing approaches by incorporating a new perspective which classifies the sarcasm based on the level of harshness employed. The major application of the proposed work would be relating the emotional state of a person to the type of sarcasm exhibited by him/her which could provide major insights about the emotional behavior of a person. An ensemble-based feature selection method has been proposed for identifying the optimal set of features needed to detect sarcasm from tweets. This optimal set of features was employed to detect whether the tweet is sarcastic or not. After detecting sarcastic sentences, a multi-rule based approach has been proposed to determine the type of sarcasm. As an initial attempt, sarcasm has been classified into four types, namely, polite sarcasm, rude sarcasm, raging sarcasm, and deadpan sarcasm. The performance and efficiency of the proposed approach has been experimentally analyzed, and change in mood of a person for each sarcastic type has been modelled. The overall accuracy of the proposed ensemble feature selection algorithm for sarcasm detection is around 92.7%, and the proposed multi-rule approach for sarcastic type identification achieves an accuracy of 95.98%, 96.20%, 99.79%, and 86.61% for polite, rude, raging, and deadpan types of sarcasm, respectively.

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Section: Related Workmentioning

confidence: 99%

Multi-Rule Based Ensemble Feature Selection Model for Sarcasm Type Detection in Twitter

Sundararajan

Palanisamy

2020

Computational Intelligence and Neuroscience

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“…In recent decades, prominent research works are carried out by various researches [17]- [21] [27] for automatic detection of irony in various micro-blogs such as twitter, product reviews and movie reviews. A brief survey on automatic sarcasm detection by Joshi et al [16] described various datasets, approaches, trends and issues in sarcasm detection.…”

Section: Related Workmentioning

confidence: 99%

“…The pragmatic feature includes symbolic or figurative texts such as emoticons of happy, sad, laughing, and crying etc., expressed in the sentences [15]. Researchers [17] [47] [51] [52] used various linguistic features to detect ironic utterances in short texts. However, identifying appropriate patterns to detect ironic utterances remains an open challenge.…”

Section: Introductionmentioning

confidence: 99%

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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“…Unigrams, semantic, psycholinguistic, and statistical features are employed together with ensemble feature selection methods to detect satiric, sarcastic and ironic content in news and customer reviews 15 . LIBSVM, Logistic Regression, Naïve Bayes, Bayesian Network, Multilayer Perceptron, C4.5, Classification and Regression Trees (CART) are implied over two satiric and one ironic dataset.…”

Section: Related Workmentioning

confidence: 99%

Implementation of Emotional Features on Satire Detection

Thu¹,

Aung²

2018

IJNDC

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Recognition of satirical language in social multimedia outlets turns out to be a trending research area in computational linguistics. Many researchers have analyzed satirical language from the various point of views: lexically, syntactically, and semantically. However, due to the ironic dimension of emotion embedded in the language, emotional study of satirical language has ever left behind. This paper proposes the emotion-based detection system for satirical figurative language processing. These emotional features are extracted using emotion lexicon: EmoLex and sentiment lexicon: VADER. Ensemble bagging technique is used to tackle the problem of ambiguous nature of emotion. Experiments are carried out on both short text and long text configurations namely news articles, Amazon product reviews, and tweets. Recognition of satirical language can aid in lessening the impact of implicit language in public opinion mining, sentiment analysis, fake news detection and cyberbullying.

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A novel automatic satire and irony detection using ensembled feature selection and data mining

Cited by 53 publications

References 37 publications

Multi-Rule Based Ensemble Feature Selection Model for Sarcasm Type Detection in Twitter

Multi-Rule Based Ensemble Feature Selection Model for Sarcasm Type Detection in Twitter

Automatic Irony Detection using Feature Fusion and Ensemble Classifier

Implementation of Emotional Features on Satire Detection

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