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arXiv · 1808.09896

Review Helpfulness Prediction with Embedding-Gated CNN

Cen Chen·Minghui Qiu·Yinfei Yang·Jun Zhou·Jun Huang·Xiaolong Li·Forrest Bao
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Abstract

Product reviews, in the form of texts dominantly, significantly help consumers finalize their purchasing decisions. Thus, it is important for e-commerce companies to predict review helpfulness to present and recommend reviews in a more informative manner. In this work, we introduce a convolutional neural network model that is able to extract abstract features from multi-granularity representations. Inspired by the fact that different words contribute to the meaning of a sentence differently, we consider to learn word-level embedding-gates for all the representations. Furthermore, as it is common that some product domains/categories have rich user reviews, other domains not. To help domains with less sufficient data, we integrate our model into a cross-domain relationship learning framework for effectively transferring knowledge from other domains. Extensive experiments show that our model yields better performance than the existing methods.

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Cen Chen, Minghui Qiu, Yinfei Yang, Jun Zhou, Jun Huang, Xiaolong Li, Forrest Bao. 2018-08-29. Review Helpfulness Prediction with Embedding-Gated CNN. https://arxiv.org/abs/1808.09896

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