Ranking Online Customer Reviews with SVR Model

• Main Authors: Hsien-You Hsieh, 謝弦祐
• Other Authors: Shih-Hung Wu
• Format: Others
• Language: zh-TW
• Published: 2015
• Online Access: http://ndltd.ncl.edu.tw/handle/80954925215874981145

碩士 === 朝陽科技大學 === 資訊工程系 === 103 === On the online E-Commerce platform, customer reviews provides valuable opinions and relevant content, which will affect the purchase behavior of other customers. Since the amount of online review grow fast, it is hard to read them all, therefore, a system that can find the reviews with better quality is necessary. In order to better understand the quality of reviews. In this paper, we proposed a system that can rank the reviews based on a set of linguistic features and a Support vector regression (SVR) model as a scorer. To evaluate our system, we collect 3730 Chinese 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 of whether the review is helpful or not. Since the voting result might be biased by voting time and total voting number. We defined 4 types of evaluation index and compare the regression result to each index. After the scheme for its best to increase its amount of data and then verified viewpoint.