Linear Regression Method for Review Aspect Rating Prediction

Abstract:

Online reviews as a new textual domain offer a unique proposition for sentiment analysis. The reviewers usually give a whole rating score to the product. The potential customers tend to make decision according to the reviews. Previous works mainly focus on the summarization of the rating and sentiment of reviews. However, they ignore an important question. The whole rating can be regarded as linear regression of different aspect ratings. High aspect rating and low aspect rating compensate each other. Therefore, previous works are coarse-grained analysis. This paper first proposed a weak supervised learning method to extract implicit aspect with aspect seeds. It then formulates the aspect rating problem as a linear regression model. Finally a gradient descent method is proposed to handle the problem. Different datasets are collected. Experimental result in the datasets demonstrates the advantage of the proposed model.

• Periodical: Applied Mechanics and Materials (Volumes 543-547)
• Pages: 3507-3510
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.543-547.3507
• Online since: March 2014
• Authors: Lan You, Qing Xi Peng*
• Keywords: Aspect, Gradient Descent, Linear Regression, Semi-Supervised Learning

References

[1] M. Hu, B. Liu: Mining and summarizing customer reviews, Proceedings of the tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. (ACM, 2004), pp.168-177.

DOI: 10.1145/1014052.1014073

[2] Brody S, Elhadad N.: An unsupervised aspect-sentiment model for online reviews, Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics. Association for Computational Linguistics (2010).

DOI: 10.3115/1620754.1620843

[3] Moghaddam S, Ester M.: On the design of LDA models for aspect-based opinion mining, Proceedings of the 21st ACM International Conference on Information and Knowledge Management (ACM, 2012), pp.803-812.

DOI: 10.1145/2396761.2396863

[4] Mukherjee A, B. Liu: Aspect extraction through semi-supervised modeling, Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers-Volume 1. Association for Computational Linguistics (2012), pp.339-348.

DOI: 10.3115/v1/p14-1033

[5] H. Wang, Y. Lu, C. Zhai: Latent aspect rating analysis on review text data: a rating regression approach, Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (ACM, 2010), pp.783-792.

DOI: 10.1145/1835804.1835903

[6] F. Li, N. Liu, H. Jin, et al.: Incorporating reviewer and product information for review rating prediction, Proceedings of the Twenty-Second International joint Conference on Artificial Intelligence (2011), Vol. 3, pp.1820-1825.

[7] Moghaddam, Samaneh, and Martin Ester: Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval (ACM, USA 2011).