Modified framework for sarcasm detection and classification in sentiment analysis

Coenen, FP ORCID: 0000-0003-1026-6649, Suhaimin, Mohd Suhairi Md, Hijazi, Mohd Hanafi Ahmad and Alfred, Rayner
(2019) Modified framework for sarcasm detection and classification in sentiment analysis. Indonesian Journal of Electrical Engineering and Computer Science, 13 (3). p. 1175.

Access the full-text of this item by clicking on the Open Access link.


<jats:p>&lt;span&gt;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.&lt;/span&gt;</jats:p>

Item Type: Article
Depositing User: Symplectic Admin
Date Deposited: 26 Feb 2019 09:41
Last Modified: 19 Jan 2023 01:01
DOI: 10.11591/ijeecs.v13.i3.pp1175-1183
Open Access URL:
Related URLs: