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Reseach Article

Optimal Feature Extraction based Machine Learning Approach for Sarcasm Type Detection in News Headlines

by Vaishvi Prayag Jariwala
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 177 - Number 46
Year of Publication: 2020
Authors: Vaishvi Prayag Jariwala
10.5120/ijca2020919981

Vaishvi Prayag Jariwala . Optimal Feature Extraction based Machine Learning Approach for Sarcasm Type Detection in News Headlines. International Journal of Computer Applications. 177, 46 ( Mar 2020), 25-29. DOI=10.5120/ijca2020919981

@article{ 10.5120/ijca2020919981,
author = { Vaishvi Prayag Jariwala },
title = { Optimal Feature Extraction based Machine Learning Approach for Sarcasm Type Detection in News Headlines },
journal = { International Journal of Computer Applications },
issue_date = { Mar 2020 },
volume = { 177 },
number = { 46 },
month = { Mar },
year = { 2020 },
issn = { 0975-8887 },
pages = { 25-29 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume177/number46/31218-2020919981/ },
doi = { 10.5120/ijca2020919981 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:48:48.349618+05:30
%A Vaishvi Prayag Jariwala
%T Optimal Feature Extraction based Machine Learning Approach for Sarcasm Type Detection in News Headlines
%J International Journal of Computer Applications
%@ 0975-8887
%V 177
%N 46
%P 25-29
%D 2020
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Sarcasm detection has received increasing research in recent years. Detection of sarcasm is of great importance and beneficial to many NLP applications, such as sentiment analysis, opinion mining and advertising. Detection of sarcasm is of great importance and beneficial to many NLP applications, such as sentiment analysis, opinion mining and advertising. Generally, sarcasm detection task is treated as standard test classification problem. Sarcasm is the unconventional way of conveying a message which conflicts the context. It can lead to a state of ambiguity. As Sarcasm represents contrary sentiment to the literal meaning that is conveyed in the text, it is hard to identify sarcasm even for a human. Existing models mainly focus on designing effective features for improving the detection performance. In this paper, optimal features are to be selected before data passes to classification task. So data pre-processing makes the data clean so that the performance of the classifier will be enhance. Result shows the improve performance in sarcasm detection using the optimal feature sets.

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Index Terms

Computer Science
Information Sciences

Keywords

Irony Satire Sentiment Analysis Sarcasm detection SVM