Paper Info
Title
Classification Of Multiple Affective Attributes Of Customer Reviews: Using Classical Machine Learning And Deep Learning
Abstract
Affective(1) engineering is a methodology of designing products by collecting customer affective needs and translating them into product designs. It usually begins with questionnaire surveys to collect customer affective demands and responses. However, this process is expensive, which can only be conducted periodically in a small scale. With the rapid development of e-commerce, a larger number of customer product reviews are available on the Internet. Many studies have been done using opinion mining and sentiment analysis. However, the existing studies focus on the polarity classification from a single perspective (such as positive and negative). The classification of multiple affective attributes receives less attention. In this paper, 3-class classifications of four different affective attributes (i. e. Soft-Hard, Appealing-Unappealing, Handy-Bulky, and Reliable-Shoddy) are performed by using two classical machine learning algorithms (i. e. Softmax regression and Support Vector Machine) and two deep learning methods (i. e. Restricted Boltzmann machines and Deep Belief Network) on an Amazon dataset. The results show that the accuracy of deep learning methods is above 90%, while the accuracy of classical machine learning methods is about 64%. This indicates that deep learning methods are significantly better than classical machine learning methods.
Year
DOI
Venue
2018
10.1145/3207677.3277953
PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND APPLICATION ENGINEERING (CSAE2018)
Keywords
DocType
Citations 
Affective analysis, natural language processing, deep belief network, restricted Boltzmann machines, support vector machine, softmax regression, Kansei engineering, Customer reviews
Conference
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
JiaWen Wang1147.94
Wai Ming Wang200.68
Zonggui Tian300.34
Zhi Li400.68