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

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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.
Multitask deep learning for sentiment analysis with sarcasm detection in bilingual code-mixed social media content Mohd Suhairi Md Suhaimin; Adi Wibowo; Ervin Gubin Moung; Patricia Anthony; Mohd Hanafi Ahmad Hijazi
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.10935

Abstract

Sentiment analysis in social media often hindered by sarcasm, which can reverse text meaning, and bilingual code-mixing, which adds complexity in non-English primary context. Existing approaches extract separate features for each language and translate them into a single language, resulting in the loss of contextual meaning and omission of crucial features. This paper proposes a multitask learning model for sentiment analysis with sarcasm detection tailored to bilingual code-mixed social media content. A hybrid feature engineering technique is integrated into a multitask deep learning architecture designed to capture the nuances of sentiment and sarcasm while addressing the complexities of processing bilingual code-mixed content. The hybrid technique combines domain-knowledge-based natural language processing (NLP) with a deep learning-based embedding approach. It includes rule-based preprocessing, normalization, spellchecking, feature extraction and selection, and feature representation. The engineered features are integrated into a multitask deep learning network using bidirectional long short-term memory (Bi-LSTM) combined with gated recurrent units (GRU). Using a public dataset that contains bilingual code-mixed social media content related to public security, our proposed model achieved a higher F1score compared to two baseline models that employ single task and multitask approaches.
Co-Authors Adi Wibowo Ervin Gubin Moung Frans Coenen Mohd Hanafi Ahmad Hijazi Patricia Anthony Rayner Alfred