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Sentic-Emotion Classifier on eWallet Reviews

Author

Listed:
  • Tong Ming Lim

    (Tunku Abdul Rahman University of Management and Technology, Malaysia)

  • Yuen Kei Khor

    (Tunku Abdul Rahman University of Management and Technology, Malaysia)

  • Chi Wee Tan

    (Tunku Abdul Rahman University of Management and Technology, Malaysia)

Abstract

Emotion classification using hybrid framework using lexicon and machine learning algorithms have been proven to be more accurate. This research analyses emotions from reviews of a popular eWallet mobile application in Malaysia. The proposed Sentic-Emotion Classifier is evaluated on its performance as it analyses the code-switched reviews crawled that contain formal and informal or out-of-vocab words. The code-switched reviews are mainly made up of words and expressions in English and Malay language models. This research designs, implements, and investigates several novel techniques that have been shown to have reliable and consistent predictive outcomes, and these outcomes are validated with manually annotated reviews so that the proposed classifier can be evaluated objectively. The novel contributions of the Sentic-Emotion Classifier consist of 2-tier sentiment classification, extended emolex framework, and multi-layer discrete emotion hierarchical classes which is hypothesized to be able to yield better accuracy for emotion and intensity prediction for the proposed framework.

Suggested Citation

  • Tong Ming Lim & Yuen Kei Khor & Chi Wee Tan, 2023. "Sentic-Emotion Classifier on eWallet Reviews," International Journal of Business Analytics (IJBAN), IGI Global, vol. 10(1), pages 1-29, January.
    • Handle: RePEc:igg:jban00:v:10:y:2023:i:1:p:1-29
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