A Study of Improving the Classification Performance on Manipulated Online Comments

• Main Authors: Ching-Yun Hsueh, 薛仱芸
• Other Authors: Long-Sheng Chen
• Format: Others
• Language: zh-TW
• Published: 2014
• Online Access: http://ndltd.ncl.edu.tw/handle/42040443048736105900

碩士 === 朝陽科技大學 === 資訊管理系碩士班 === 102 === With the proliferation of e-commerce, internet has become an ex-cellent platform for gathering and sharing consumers’ personal views on, preferences for, and experiences with products. With the popularity of text based communication tools, customers can easily express their opinions about purchased products or services. Generally speaking, the on-line reviews should be unbiased reflections of the consumers’ expe-riences with the products or services. However, some comments are biased “manipulation”, which might reduce consumers’ purchase in-tentions and bring a great damage to enterprisers. This study aims to improve the performance for manipulation detection through reducing dimension space. The study is divided into three parts. In the first part, we introduce feature selection and feature extraction techniques, the important feature is based on Information gain, Global-LSI, Local-LSI is reduced by the dimension, to improve the detection accuracy further. In the second part, we adopt 11 features recommended in the literature, and show that these features can improve the detection rate significantly. In the third part, we try to find the most important feature attributes in 11 manipulation feature. A real case study of smart phone is used to illustrate the effectiveness of the proposed features.