Integrating Neural and Syntactic Features on the Helpfulness Analysis of the Online Customer Reviews

Before purchasing a product online, customers often read the reviews posted by people who also brought the product. Customer reviews provide opinions and relevant information such as comparisons among similar products or usage experiences about the product. Previous studies addressed on the prediction of the helpfulness of customer reviews to predict the helpfulness voting results. However, the voting result of an online review is not a constant over time; predicting the voting result based on the analysis of text is not practical. Therefore, we collect the voting results of the same online customer review over time, and observe whether the number of votes will increase or not. We construct a dataset with 10,195 online reviews in six different product categories (Computer Hardware, Drink, Makeup, Pen, Shoes, and Toys) from Amazon.cn with the voting result on the helpfulness of the reviews, and monitor the helpfulness voting in six weeks. Experiments are conducted on the dataset to predict whether the helpfulness voting result of each review will increase or not. We propose a classification system that can classify the online reviews into more helpful ones, based on a set of syntactic features and neural features trained via CNN. The results show that integrating the syntactic features with the neural features can get better result.