Effective analysis of emotion-based satire detection model on various machine learning algorithms

Thu, Pyae Phyo; Aung, Than Nwe

doi:10.1109/gcce.2017.8229386

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Emotions and Satire Detection1

Preprocessing Steps1

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Cited by 5 publications

(2 citation statements)

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“…For this reason, it is relevant to investigate the relationship between the emotion expressed in a text and the detection of satire. An interesting approach has been reported in [35], where a corpus of satirical and non-satirical news articles has been analyzed to detect satire by exploiting sentiment analysis and a social cognition engine.…”

Section: Emotions and Satire Detectionmentioning

confidence: 99%

Supporting Emotion Automatic Detection and Analysis over Real-Life Text Corpora via Deep Learning: Model, Methodology and Framework

Cuzzocrea¹,

Bosco²,

Maiorana³

et al. 2021

JVLC

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Section: Emotions and Satire Detectionmentioning

confidence: 99%

Supporting Emotion Automatic Detection and Analysis over Real-Life Text Corpora via Deep Learning: Model, Methodology and Framework

Cuzzocrea¹,

Bosco²,

Maiorana³

et al. 2021

JVLC

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“…• Sentiment features For both negative and positive phrases [11], sentiment scores are being provided. Three classes are being used i.e.…”

Section: Preprocessing Stepsmentioning

confidence: 99%

AI-Based Learning Techniques for Sarcasm Detection of Social Media Tweets: State-of-the-Art Survey

Kumar

Goel

2020

SN COMPUT. SCI.

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Sarcasm, though difficult to define but plays a crucial role in one's life. Sarcasm as a jest is a matter of fun but when taken seriously can cause unwelcoming results. Sometimes, sarcasm is defined as "a sharp, bitter, or cutting expression or remark; a bitter jibe or taunt". These days' researchers are working towards the detection of sarcasm for the purpose of sentiment analysis. Emotion and sentiment-bearing information are carried by subjective sarcastic sentences. The objective of the paper is to highlight the different types of sarcastic tweets and their usage in sentiment analysis. The authors mainly emphasize several approaches which include sentiment analysis, machine and deep learning classifications. The paper focuses on the use of machine learning and deep learning for identifying sarcastic tweets. Numerous feature extraction techniques have been studied and machine and deep learning classifications have been taken into account. The comparative table shows the results obtained using the various evaluation metrics such as accuracy, precision, recall, and f-score.

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Polarity classification on twitter data for classifying sarcasm using clause pattern for sentiment analysis

Prasanna

Shaila

Vadivel

2023

Multimed Tools Appl

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Effective analysis of emotion-based satire detection model on various machine learning algorithms

Cited by 5 publications

References 5 publications

Supporting Emotion Automatic Detection and Analysis over Real-Life Text Corpora via Deep Learning: Model, Methodology and Framework

Supporting Emotion Automatic Detection and Analysis over Real-Life Text Corpora via Deep Learning: Model, Methodology and Framework

AI-Based Learning Techniques for Sarcasm Detection of Social Media Tweets: State-of-the-Art Survey

Polarity classification on twitter data for classifying sarcasm using clause pattern for sentiment analysis

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