Open AccessDetection on sarcasm using machine learning classifiers and rule based approach
Author(s) -
K. Sentamilselvan,
P. Suresh,
G. K. Kamalam,
Siddharth Mahendran,
D. Aneri
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1055/1/012105
Subject(s) - artificial intelligence , computer science , support vector machine , sarcasm , irony , machine learning , sentiment analysis , natural language processing , naive bayes classifier , random forest , preprocessor , lexical analysis , linguistics , philosophy
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.