Modelling customer satisfaction from online reviews using ensemble neural network and effect-based Kano model

Bi, Jian-Wu; Liu, Yang; Fan, Zhi‐Ping; Wang, Zhaoxia

doi:10.1080/00207543.2019.1574989

Cited by 152 publications

(107 citation statements)

References 53 publications

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“…Otherwise a big data application can have a present and predictive orientation (Lee, 2017;Priya and Ranjith Kumar, 2015;van der Spoel et al, 2017), such as real-time trading tools. Finally, a big data application can have a future, hence prescriptive (Amankwah-Amoah, 2016), orientation for example in strategic decision-support systems (Bi et al, 2019a(Bi et al, , 2019bGunasekaran et al, 2017). The various possible combinations explain why BDA solutions could potentially provide business value in the most diverse activities of any organisation (Tan et al, 2015;Wamba et al, 2015;Wang et al, 2018).…”

Section: Effect Of the Business Value Of Bda Solutions On Firm Performentioning

confidence: 99%

“…Hence, research is needed to face the enormous challenge of knowing how big data can be used to support decision-making (Bi et al, 2019a(Bi et al, , 2019bLi et al, 2016;Matthias et al, 2017;Tan et al, 2017), finding a positive Return On Investment on the large investments required in this domain, which could otherwise jeopardise the entire organisation (Braganza et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

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Big data analytics business value and firm performance: linking with environmental context

Vitari

Raguseo

2019

International Journal of Production Research

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HAL is a multidisciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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Section: Effect Of the Business Value Of Bda Solutions On Firm Performentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Big data analytics business value and firm performance: linking with environmental context

Vitari

Raguseo

2019

International Journal of Production Research

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“…A new neural network approach proposed by Tang et al [25], incorporating user-level and product-level information, worked for sentiment classification. Bi et al [26] proposed an ensemble neural network-based model (ENNM), which could analyse complex relationships among factors obtained from online reviews, to measure the effects of customer sentiments toward different customer satisfaction dimensions (CSDs) on customer satisfaction. An effect-based Kano model (EKM) was proposed and used to classify the CSDs into different categories.…”

Section: Related Workmentioning

confidence: 99%

Design of Festival Sentiment Classifier Based on Social Network

Yuan

Song

et al. 2020

Computational Intelligence and Neuroscience

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With the development of society, more and more attention has been paid to cultural festivals. In addition to the government’s emphasis, the increasing consumption in festivals also proves that cultural festivals are playing increasingly important role in public life. Therefore, it is very vital to grasp the public festival sentiment. Text sentiment analysis is an important research content in the field of machine learning in recent years. However, at present, there are few studies on festival sentiment, and sentiment classifiers are also limited by domain or language. The Chinese text classifier is much less than the English version. This paper takes Sina Weibo as the text information carrier and Chinese festival microblogs as the research object. CHN-EDA is used to do Chinese text data augmentation, and then the traditional classifiers CNN, DNN, and naïve Bayes are compared to obtain a higher accuracy. The matching optimizer is selected, and relevant parameters are determined through experiments. This paper solves the problem of unbalanced Chinese sentiment data and establishes a more targeted festival text classifier. This festival sentiment classifier can collect public festival emotion effectively, which is beneficial for cultural inheritance and business decisions adjustment.

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“…The sentiment analysis has vast applications in different fields of life such as social science, political sciences [2,3], and marketing [4], in e-commerce and for customer satisfaction [5,6]. Sentiment based analysis of online reviews regarding products and services also helps in understanding satisfaction of customers [7]. E-commerce has given opportunity to customers to review products online.…”

Section: Introductionmentioning

confidence: 99%

Exploring Diverse Features for Sentiment Quantification Using Machine Learning Algorithms

et al. 2020

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In the era of web 2.0, online forums, blogs and Twitter are becoming primary sources for sharing views, opinions and comments about different topics. Classifying these views, opinions and comments is known as sentiment analysis which is an active research area. Sentiment analysis has vast applications in different fields of life, such as marketing, e-commerce and business. Under the umbrella of sentiment analysis, sentiment quantification that deals with estimating relative frequency of class of interest is being investigated by researchers nowadays. In sentiment quantification, exploring effect of new features and comparison of diverse types of classifiers to assess their effectiveness needs further investigation. In this paper, we explore diverse feature sets and classifiers for sentiment quantification. In addition, empirical performance analysis of conventional machine learning techniques, ensemble-based methods and state-ofthe-art deep learning algorithms on basis of features set, is performed. The computed results show that the diverse features sets affect the performance of classifiers in sentiment quantification. The results also confirm that the deep learning techniques perform better than the conventional machine learning algorithms.

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Modelling customer satisfaction from online reviews using ensemble neural network and effect-based Kano model

Cited by 152 publications

References 53 publications

Big data analytics business value and firm performance: linking with environmental context

Big data analytics business value and firm performance: linking with environmental context

Design of Festival Sentiment Classifier Based on Social Network

Exploring Diverse Features for Sentiment Quantification Using Machine Learning Algorithms

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