Sarcasm Detection And Classification To Support Sentiment Analysis: A Study In Malay Social Media - Mohd Hanafi Ahmad Hijazi (2016)
This research work conducted on sarcasm detection and classification to support sentiment analysis. The proposed work consists of two phases: (i) sarcasm detection and (ii) sentiment analysis with sarcasm detection and classification. In the first phase, the development of a mechanism for detecting sarcasm on bilingual data was explored. To achieve this, a feature extraction process was proposed to identify sarcasm features. Five feature categories that can be extracted using natural language processing were considered. The best-performing features were then used as input for the second phase. In the second phase, a framework for sentiment analysis that considers sarcasm detection and classification was proposed. Results obtained demonstrate that the proposed features and framework are able to improve the performance of sentiment analysis.