Predicting customer satisfaction based on online reviews and hybrid ensemble genetic programming algorithms

2020 ◽  
Vol 95 ◽  
pp. 103902
Author(s):  
Kit Yan Chan ◽  
C.K. Kwong ◽  
Gül E. Kremer
Keyword(s):  
Customer Satisfaction ◽  
Genetic Programming ◽  
Online Reviews ◽  
Programming Algorithms

scholarly journals Penalty Functions for Genetic Programming Algorithms

2011 ◽  
pp. 550-562 ◽  
Author(s):  
José L. Montaña ◽  
César L. Alonso ◽  
Cruz Enrique Borges ◽  
Javier de la Dehesa
Keyword(s):  
Genetic Programming ◽  
Penalty Functions ◽  
Programming Algorithms

scholarly journals Research on the Influence of E-commerce service quality of fresh Agricultural products on customer satisfaction

2020 ◽  
Vol 189 ◽  
pp. 01022
Author(s):  
Xu xin ◽  
Chen jiaying
Keyword(s):  
Customer Satisfaction ◽  
Service Quality ◽  
Significant Influence ◽  
Online Reviews ◽  
Service Level ◽  
Competitive Advantages ◽  
Web Crawler ◽  
Evaluation Indexes ◽  
Healthy Development

Analyzing the influence of service quality on customer satisfaction can help fresh e-commerce enterprises to better understand their own service level, formulate better service strategies and improve their competitive advantages, so as to promote the sustainable and healthy development of fresh e-commerce industry. In this paper, first of all, with the aid of web crawler, acquisition of jingdong mall fresh category contains fruit, vegetables, meat and seafood aquaculture 4 products on the number of online comments and evaluation star, and word frequency statistics and extract the data collected from online reviews of consumers to pay attention to the quality of service measures, after using qualitative analysis software - NVivo coding and grade, finally, the variable of descriptive statistics, correlation analysis and regression analysis. The results show that the tangibility, reliability, empathy and responsiveness of service quality have significant influence on customer satisfaction, and other evaluation indexes of guarantee quality have significant influence on customer satisfaction except delivery.

scholarly journals Influence of Experiment Design in GPA Investigating with Respect to PRNGs

MENDEL ◽  
2018 ◽  
Vol 24 (2) ◽  
Author(s):  
Tomas Brandejsky
Keyword(s):  
Genetic Programming ◽  
Random Number ◽  
Experiment Design ◽  
Parallel Execution ◽  
Random Number Generators ◽  
Pseudo Random Number ◽  
Programming Algorithms

This paper analyses the influence of experiment parameters onto the reliability of experiments with genetic programming algorithms. The paper is focused on the required number of experiments and especially on the influence of parallel execution which affect not only the order of thread execution but also behaviors of pseudo random number generators, which frequently do not respect recommendation of C++11 standard and are not implemented as thread safe. The observations and the effect of the suggested improvements are demonstrated on results of 720,000 experiments.

Determining banking service attributes from online reviews: text mining and sentiment analysis

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Divya Mittal ◽  
Shiv Ratan Agrawal
Keyword(s):  
Text Mining ◽  
Customer Satisfaction ◽  
Sentiment Analysis ◽  
Banking Sector ◽  
Online Reviews ◽  
Socioeconomic Background ◽  
Content Type ◽  
Customer Reviews ◽  
Banking Service ◽  
The Interest Rate

PurposeThe current study employs text mining and sentiment analysis to identify core banking service attributes and customer sentiment in online user-generated reviews. Additionally, the study explains customer satisfaction based on the identified predictors.Design/methodology/approachA total of 32,217 customer reviews were collected across 29 top banks on bankbazaar.com posted from 2014 to 2021. In total three conceptual models were developed and evaluated employing regression analysis.FindingsThe study revealed that all variables were found to be statistically significant and affect customer satisfaction in their respective models except the interest rate.Research limitations/implicationsThe study is confined to the geographical representation of its subjects' i.e. Indian customers. A cross-cultural and socioeconomic background analysis of banking customers in different countries may help to better generalize the findings.Practical implicationsThe study makes essential theoretical and managerial contributions to the existing literature on services, particularly the banking sector.Originality/valueThis paper is unique in nature that focuses on banking customer satisfaction from online reviews and ratings using text mining and sentiment analysis.

Looking beyond the stars: A description of text mining technique to extract latent dimensions from online product reviews

2019 ◽  
Vol 62 (2) ◽  
pp. 195-215
Author(s):  
Frederik Situmeang ◽  
Nelleke de Boer ◽  
Austin Zhang
Keyword(s):  
Text Mining ◽  
Customer Satisfaction ◽  
Research Methodology ◽  
Latent Dirichlet Allocation ◽  
Online Reviews ◽  
Product Reviews ◽  
Marketing Literature ◽  
Mining Technique ◽  
Online Product Reviews ◽  
The Relationship

The purpose of this study is to contribute to the marketing literature and practice by describing a research methodology to identify latent dimensions of customer satisfaction in product reviews, and examining the relationship between these attributes and customer satisfaction. Previous research in product reviews has largely relied only on quantitative ratings, either stars or review score. Advanced techniques for text mining provide the opportunity to extract meaning from customer online reviews. By analyzing 51,110 online reviews for 1,610 restaurants via latent Dirichlet allocation, this study uncovers 30 latent dimensions that are determinants of customer satisfaction. Furthermore, this study developed measurements of sentiment and innovativeness as moderators of the effect of these latent attributes to satisfaction.

How managerial responses to online reviews affect customer satisfaction: An empirical study based on additional reviews

2020 ◽  
Vol 57 ◽  
pp. 102205 ◽  
Author(s):  
Yan Zhao ◽  
Lingling Wen ◽  
Xiangnan Feng ◽  
Ran Li ◽  
Xiaolin Lin
Keyword(s):  
Customer Satisfaction ◽  
Empirical Study ◽  
Online Reviews
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