ISSN: 0000-0000 e-ISSN: 0000-0000
Open Access

Aspect-Based Sentiment Analysis for Product Reviews

1 Vietnam National University, Vietnam
2 AGH University of Science and Technology, Poland
3 Dokuz Eylul University, Turkey

Abstract

This comprehensive review examines the current state and future prospects of this research area. We analyze over 122 studies published in recent years, covering applications in various domains. The review identifies key technological advancements, validation challenges, and regulatory considerations. Our analysis reveals significant progress in the field.

Keywords

How to Cite

Tran, N., Nowak, T., & Yıldız, C. (2022). Aspect-Based Sentiment Analysis for Product Reviews. Nivo Light - International Journal of Research & Innovation, 4(3), 46–50. https://doi.org/10.28051/ojstest-138

References

📄 Smith, J., & Brown, A. (2018). Deep learning applications in urban computing. Journal of Smart Cities, 15(3), 234-251.
📄 Wang, L., Chen, H., & Liu, X. (2017). Energy optimization algorithms for intelligent buildings. Energy and Buildings, 142, 45-58.
📄 García, M., Rodriguez, P., & Martinez, S. (2018). Sustainable urban development: A comprehensive review. Sustainability Science, 13(2), 189-205.
📄 Tanaka, K., & Yamamoto, H. (2016). Smart grid technologies and renewable energy integration. IEEE Transactions on Smart Grid, 7(4), 1892-1901.
📄 Anderson, R., Williams, T., & Davis, M. (2017). Machine learning for demand response systems. Applied Energy, 201, 112-125.

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