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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Mohd Suhairi Md Suhaimin
Universiti Malaysia Sabah

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Modified framework for sarcasm detection and classification in sentiment analysis Mohd Suhairi Md Suhaimin; Mohd Hanafi Ahmad Hijazi; Rayner Alfred; Frans Coenen
Indonesian Journal of Electrical Engineering and Computer Science Vol 13, No 3: March 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v13.i3.pp1175-1183

Abstract

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.
Co-Authors Frans Coenen Mohd Hanafi Ahmad Hijazi Rayner Alfred