Importance-Performance Analysis of Product Attributes Using Explainable Deep Neural Network From Online Reviews

2020 ◽  
Author(s):  
Junegak Joung ◽  
Harrison M. Kim
Keyword(s):  
Neural Network ◽  
Performance Analysis ◽  
Network Architecture ◽  
Deep Neural Network ◽  
Online Reviews ◽  
Product Attributes ◽  
Neural Network Architecture ◽  
Single Neural Network

Abstract Importance-performance analysis (IPA) is a technique used to understand customer satisfaction and improve the quality of product attributes. This study proposes an explainable deep-neural-network-based method to carry out IPA of product attributes from online reviews for product design. Previous works used shallow neural network (SNN)-based methods to estimate importance values, but it was unclear whether the SNN is an optimal neural network architecture. The estimated importance has high variability by a single neural network from a training set that is randomly selected. However, the proposed method provides importance values with a lower variance by improving the importance estimation of each product attribute in the IPA. The proposed method first identifies the product attributes and estimates their performance. Then, it infers the importance values by combining explanations of the input features from multiple optimal neural networks. A case study on smartphones is used herein to demonstrate the proposed method.

Approach for Importance–Performance Analysis of Product Attributes From Online Reviews

2021 ◽  
Vol 143 (8) ◽  
Author(s):  
Junegak Joung ◽  
Harrison M. Kim
Keyword(s):  
Neural Network ◽  
Performance Analysis ◽  
Sentiment Analysis ◽  
High Performance ◽  
Latent Dirichlet Allocation ◽  
Online Reviews ◽  
Product Attributes ◽  
Ibm Watson ◽  
Promising Source

Abstract The importance–performance analysis (IPA) is a widely used technique to guide strategic planning for the improvement of customer satisfaction. Compared with surveys, numerous online reviews can be easily collected at a lower cost. Online reviews provide a promising source for the IPA. This paper proposes an approach for conducting the IPA from online reviews for product design. Product attributes from online reviews are first identified by latent Dirichlet allocation. The performance of the identified attributes is subsequently estimated by the aspect-based sentiment analysis of IBM Watson. Finally, the importance of the identified attributes is estimated by evaluating the effect of sentiments of each product attribute on the overall rating using an explainable deep neural network. A Shapley additive explanation-based method is proposed to estimate the importance values of product attributes with a low variance by combining the effect of the input features from multiple optimal neural networks with a high performance. A case study of smartphones is presented to demonstrate the proposed approach. The performance and importance estimates of the proposed approach are compared with those of previous sentiment analysis and neural network-based method, and the results exhibit that the former can perform IPA more reliably. The proposed approach uses minimal manual operation and can support companies to take decisions rapidly and effectively, compared with survey-based methods.

scholarly journals USE OF GENETIC ALGORITHM IN DEEP NEURAL NETWORKS CONFIGURATION FOR THE PURPOSES OF COMPUTER ATTACKS CLASSIFICATION

2020 ◽  
pp. 104-117
Author(s):  
O.S. Amosov ◽  
◽  
S.G. Amosova ◽  
D.S. Magola ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Network Architecture ◽  
Deep Neural Network ◽  
Deep Neural Networks ◽  
Classification Problem ◽  
Neural Network Architecture ◽  
Computer Attacks ◽  
Neural Network Technology

The task of multiclass network classification of computer attacks is given. The applicability of deep neural network technology in problem solving has been considered. Deep neural network architecture was chosen based on the strategy of combining a set of convolution and recurrence LSTM layers. Op-timization of neural network parameters based on genetic algorithm is proposed. The presented results of modeling show the possibility of solving the network classification problem in real time.

scholarly journals Varmole: a biologically drop-connect deep neural network model for prioritizing disease risk variants and genes

2020 ◽  
Author(s):  
Nam D Nguyen ◽  
Ting Jin ◽  
Daifeng Wang
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Deep Neural Network ◽  
Molecular Mechanisms ◽  
Genome Wide Association Study ◽  
Disease Risk ◽  
Supplementary Information ◽  
Neural Network Architecture ◽  
Risk Variants ◽  
Genome Wide

Abstract Summary Population studies such as genome-wide association study have identified a variety of genomic variants associated with human diseases. To further understand potential mechanisms of disease variants, recent statistical methods associate functional omic data (e.g. gene expression) with genotype and phenotype and link variants to individual genes. However, how to interpret molecular mechanisms from such associations, especially across omics, is still challenging. To address this problem, we developed an interpretable deep learning method, Varmole, to simultaneously reveal genomic functions and mechanisms while predicting phenotype from genotype. In particular, Varmole embeds multi-omic networks into a deep neural network architecture and prioritizes variants, genes and regulatory linkages via biological drop-connect without needing prior feature selections. Availability and implementation Varmole is available as a Python tool on GitHub at https://github.com/daifengwanglab/Varmole. Supplementary information Supplementary data are available at Bioinformatics online.

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