Learning to Rank for Review Rating Prediction

Nian Li; Li Yin; Qing Xi Peng

doi:10.4028/www.scientific.net/AMM.556-562.6286

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 556-562Learning to Rank for Review Rating Prediction

Learning to Rank for Review Rating Prediction

Abstract:

The Internet has experienced profound changes. Large amount of user-generated-contents provide valuable information to the public. Customers usually express their opinion in online shopping. After they finish the reviews, they give an overall rating to the product or service. In this paper, we focus on the review rating prediction problem. Previous studies usually regard this problem as a regression problem. We take a new machine learning method to solve the problem. Learning to rank method has been exploited to tackle the prediction. After feature selection, the maximum entropy classifier has been employed to solve the multi-classification problem. The real life dataset has been crawled to verify the proposed method. Empirical studies demonstrate the proposed method outperform the baseline methods.

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Periodical:

Applied Mechanics and Materials (Volumes 556-562)

Pages:

6286-6289

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.556-562.6286

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Online since:

May 2014

Authors:

Nian Li, Li Yin, Qing Xi Peng*

Keywords:

Information Gain, Learning to Rank, Maximum Entropy Model, Prediction

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* - Corresponding Author

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