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Joint sentence and aspect-level sentiment analysis of product comments

Author

Listed:
  • Long Mai

    (University of Science
    Vietnam National University)

  • Bac Le

    (University of Science
    Vietnam National University)

Abstract

Comments from social media platforms (such as YouTube) have become a valuable resource for manufacturers to examine public opinion toward their products. Accordingly, we propose a novel framework for automatically collecting, filtering, and analyzing comments from YouTube for a given product. First, we devise a classification scheme to select relevant and high-quality comments from retrieval results. These comments are then analyzed in a sentiment analysis, where we introduce a joint approach to perform a combined sentence and aspect level sentiment analysis. Hence, we can achieve the following: (1) capture the mutual benefits between these two tasks, and (2) leverage knowledge learned from solving one task to solve another. Experiment results on our dataset show that the joint model achieves a satisfactory performance and outperforms the separate one on both sentence and aspect levels. Our framework does not require feature engineering efforts or external linguistic resources; therefore, it can be adapted for many languages without difficulties.

Suggested Citation

  • Long Mai & Bac Le, 2021. "Joint sentence and aspect-level sentiment analysis of product comments," Annals of Operations Research, Springer, vol. 300(2), pages 493-513, May.
    • Handle: RePEc:spr:annopr:v:300:y:2021:i:2:d:10.1007_s10479-020-03534-7
    DOI: 10.1007/s10479-020-03534-7
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    References listed on IDEAS

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    1. César Alfaro & Javier Cano-Montero & Javier Gómez & Javier Moguerza & Felipe Ortega, 2016. "A multi-stage method for content classification and opinion mining on weblog comments," Annals of Operations Research, Springer, vol. 236(1), pages 197-213, January.
    2. Nishikant Mishra & Akshit Singh, 2018. "Use of twitter data for waste minimisation in beef supply chain," Annals of Operations Research, Springer, vol. 270(1), pages 337-359, November.
    3. César Alfaro & Javier Cano-Montero & Javier Gómez & Javier M. Moguerza & Felipe Ortega, 2016. "A multi-stage method for content classification and opinion mining on weblog comments," Annals of Operations Research, Springer, vol. 236(1), pages 197-213, January.
    4. Hui Yuan & Wei Xu & Qian Li & Raymond Lau, 2018. "Topic sentiment mining for sales performance prediction in e-commerce," Annals of Operations Research, Springer, vol. 270(1), pages 553-576, November.
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    Cited by:

    1. Saridakis, Charalampos & Katsikeas, Constantine S. & Angelidou, Sofia & Oikonomidou, Maria & Pratikakis, Polyvios, 2023. "Mining Twitter lists to extract brand-related associative information for celebrity endorsement," European Journal of Operational Research, Elsevier, vol. 311(1), pages 316-332.
    2. Nan Yang & Nikolaos Korfiatis & Dimitris Zissis & Konstantina Spanaki, 2024. "Incorporating topic membership in review rating prediction from unstructured data: a gradient boosting approach," Annals of Operations Research, Springer, vol. 339(1), pages 631-662, August.

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