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Customer reviews provide opinions and relevant information that will affect the purchase behavior of other customers. Many studies have focused on the prediction of the helpfulness rate of customer reviews to find the helpful reviews which are traditionally determined by the helpful voting results. In our study, we find that the helpfulness voting result of an online review is not constant over time. Therefore, predicting the voting result based on the analysis of text is not enough; the temporal issue must be considered. We propose a system that can rank the reviews based on a set of linguistic features with a linear regression model. To evaluate our system, we collect Chinese custom reviews in eight product categories (books, digital cameras, tablet PC, backpacks, movies, men shoes, toys, and cell phones) from Amazon.cn with the voting result on the helpfulness of the reviews. Since the voting result may be affected by voting time and total voting number, we define a new evaluation index and compare the regression results. The results show that the system has less prediction error when it takes the time information into the prediction model.

Social media has become the most convenient space to retrieve the tremendous consumers' experience, opinion and preference–toward each brands, products or even specific features. The real-time and big amount characteristics of Social media provide great opportunity for producer to know their customers (and potential ones) well. This paper proposes an Online to Offline (O2O) Commerce Service Model and takes the social relationship dashboard as an pilot study, which can help retailers or brands to understand their customers via social network existing data (especially Facebook for this case) by which we can adapt the current social commerce marketing strategy more quickly and responsively.