An Emoticon-Based Novel Sarcasm Pattern Detection Strategy to Identify Sarcasm in Microblogging Social Networks

Datos Bibliográficos

ID22106955
AutoresM Nirmala (0000-0002-1166-7885, Vellore Institute of Technology University), Amir H Gandomi (0000-0002-2798-0104, University of Technology Sydney), M Rajasekhara Babu (0000-0002-3557-1917, Vellore Institute of Technology University), L D Dhinesh Babu (0000-0002-3354-8713, Vellore Institute of Technology University), Rizwan Patan (0000-0003-4878-1988, Kennesaw State University)
Año2024
Volumen11
Número4
Páginas5319-5326
Fecha de publicación2024-08-01
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaIEEE Transactions on Computational Social Systems (JOURNAL)
Identificadores de la revistaISSN: 2329-924X • E-ISSN: 2373-7476
EditorialInstitute of Electrical and Electronics Engineers (IEEE) (PUBLISHER)
DOI10.1109/tcss.2023.3306908
OpenAlexW4386524143
IdiomaEN
Citas recibidas2
Referencias citadas18

Online social networks are one of the prime modes of communication used by people to voice their opinions and sentiments, especially after the advancement of digital gadgets and overall technology. Mining such sentiments and analyzing the polarity of user opinions is a trending research issue with high business value. Identifying, detecting, and understanding sarcasm is an important topic in the field of sentiment analysis. Despite being complex and challenging, automated detection of sarcasm is also a relatively less explored research area. In this article, we present a novel sarcasm pattern detection technique using emoticons to identify sarcasm in microblogging social networks like Twitter. Initially, we classify the tweets only with emoticons based on a decision tree classification approach. Afterward, we incorporate the SentiWordNet library and a separate emoticon library to find the polarities of the tokenized words and emoticons. Finally, we present a comparison of the polarity of the tweets and the polarity of the emoticons to detect sarcasm in tweets

Irony · Linguistics · Machine learning · Microblogging · Natural language processing · Sarcasm · Social media · World Wide Web · Computer Science · Digital Communication and Language · Sentiment Analysis and Opinion Mining · Spam and Phishing Detection · Artificial Intelligence