Detection on sarcasm using machine learning classifiers and rule based approach

Content Provider	Scilit
Author	Sentamilselvan, K. Suresh, P. Kamalam, G. K. Mahendran, S. Aneri, D.
Copyright Year	2021
Description	Journal: Iop Conference Series: Materials Science and Engineering Sentiment Analysis has been mainly used to understand the judgment of the text. It has been undergoing major provocation and irony detection is considered as one among the most provocations in it. Irony is the unusual way of narrating an information which disagrees the concept which leads to uncertainty. One primary task included by most developers is data preprocessing which includes many techniques like lemmatization, tokenization and stemming. Many researches are done under irony detection which includes many feature extraction techniques. Machine learning classifiers used for these researches are Support Vector Machine (SVM), linear regression, Naïve Bayes, Random Forest and many more. Results of these research works includes accuracy, precision, recall, F-score which can be used to predict the best suited model. In this paper various methodology used in irony text detection for Sentiment Analysis is discussed.
Related Links	https://iopscience.iop.org/article/10.1088/1757-899X/1055/1/012105/pdf
ISSN	17578981
e-ISSN	1757899X
DOI	10.1088/1757-899x/1055/1/012105
Journal	Iop Conference Series: Materials Science and Engineering
Issue Number	1
Volume Number	1055
Language	English
Publisher	IOP Publishing
Publisher Date	2021-02-01
Access Restriction	Open
Subject Keyword	Journal: Iop Conference Series: Materials Science and Engineering Hardware and Architecture Irony Detection Learning Classifiers Machine Learning
Content Type	Text
Resource Type	Article