Open AccessModified framework for sarcasm detection and classification in sentiment analysis
Author(s) -
Mohd Suhairi Md Suhaimin,
Mohd Hanafi Ahmad Hijazi,
Rayner Alfred,
Frans Coenen
Publication year - 2019
Publication title -
indonesian journal of electrical engineering and computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.241
H-Index - 17
eISSN - 2502-4760
pISSN - 2502-4752
DOI - 10.11591/ijeecs.v13.i3.pp1175-1183
Subject(s) - sarcasm , sentiment analysis , artificial intelligence , context (archaeology) , computer science , support vector machine , malay , natural language processing , social media , psychology , machine learning , irony , linguistics , history , world wide web , philosophy , archaeology
Sentiment analysis is directed at identifying people's opinions, beliefs, views and emotions in the context of the entities and attributes that appear in text. The presence of sarcasm, however, can significantly hamper sentiment analysis. In this paper a sentiment classification framework is presented that incorporates sarcasm detection. The framework was evaluated using a non-linear Support Vector Machine and Malay social media data. The results obtained demonstrated that the proposed sarcasm detection process could successfully detect the presence of sarcasm in that better sentiment classification performance was recorded. A best average F-measure score of 0.905 was recorded using the framework; a significantly better result than when sentiment classification was performed without sarcasm detection.