Customer feedbacks and opinion surveys analysis in the automotive industry

Grivel, Luc

doi:10.2495/978-1-85312-995-7/13

Cited by 4 publications

(4 citation statements)

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“…Previously mentioned experiments used the Bag of words model for analyzing the frequency of a word in documents. Grivel (2006) observed that structured linguistic analysis shows more accuracy than stochastic analysis (see Figure 2). NB is more suitable for text classification experiments because of its smooth performance and efficiency (Kim et al, 2002).…”

Section: Text Miningmentioning

confidence: 96%

Section: Literature Reviewmentioning

confidence: 96%

“…SVM showed the best performance with an equal error rate of 0.0193. Grivel (2006) represented a text mining approach for categorizing customer feedback on new cars. Data were collected from phone survey documents and phone calls.…”

Section: Text Miningmentioning

confidence: 99%

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Application of classification models on maintenance records through text mining approach in industrial environment

Rahman

Mahbub

2022

JQME

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PurposeThe data created from regular maintenance activities of equipment are stored as text in industrial plants. The size of these data is increasing rapidly nowadays. Text mining provides a chance to handle this huge amount of text data and extract meaningful information to improve various processes of an industrial environment. This paper represents the application of classification models on maintenance text records to classify failure for improving maintenance programs in the industry.Design/methodology/approachThis paper is presented as an implementation study, where text mining approaches are used for binary classification of text data. Naive Bayes and Support Vector Machine (SVM), two classification algorithms are applied for training and testing of the models as per the labeled data. The reason behind this is, these algorithms perform better on text data for classifying failure and they are easy to handle. A methodology is proposed for the development of maintenance programs, including classification of potential failure in advance by analyzing the regular maintenance data as well as comparing the performance of both models on the data.FindingsThe accuracy of both models falls within the acceptable limit, and performance evaluation of the models concludes the validation of the results. Other performance measures exhibit excellent values for both of the models.Practical implicationsThe proposed approach provides the maintenance team an opportunity to know about the upcoming breakdown in advance so that necessary measures can be taken to prevent failure in an industrial environment. As predictive maintenance incurs a high expense, it could be a better replacement for small and medium industrial plants.Originality/valueNowadays, maintenance is preventive-based rather than a corrective approach. The proposed technique is facilitating the concept of a proactive approach by minimizing the cost of additional maintenance steps. As predictive maintenance is efficient but incurs high expenses, this proposed method can minimize unnecessary maintenance operations and keep control over the budget. This is a significant way of developing maintenance programs and will make maintenance personnel ready for the machine breakdown.

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Section: Text Miningmentioning

confidence: 96%

Section: Literature Reviewmentioning

confidence: 96%

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Application of classification models on maintenance records through text mining approach in industrial environment

Rahman

Mahbub

2022

JQME

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“…In some studies such as [4], [6], [12], the ontology technology, which enables the data to be kept to allow inference, has been proposed as a solution for the integrated interpretation of data in CRM systems. However, these studies focused on eliminating customer dissatisfaction, solving customer problems and creating customer processes.…”

Section: Introductionmentioning

confidence: 99%

An Ontology Based Approach for Next Generation Customer Relationship Management Systems

Tiryaki

Atalay

2020

International Journal of Applied Mathematics Electronics and Computers

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Customer relationship management -CRM systems aim to increase customer satisfaction and loyalty to the company through actions that can be performed by monitoring and analyzing customer behaviours for companies. In these systems, the data obtained from different sources such as sales process, service, and call center are interpreted by the CRM experts as a whole, and the necessary action is taken for the customer. With the rapid progress in information technologies, the diversity of channels that are used to collect data, the size and dynamism of the data have increased. This situation has made it impossible for experts to wholly interpret big-size data from a wide variety of sources in a short period of time. In the CRM area, there is a need for technologies that automatically integrate the data obtained from several sources and can interpret it semantically. In this study, an ontology-based approach is proposed, which allows automatic semantic interpretations on the data obtained from different sources. Based on the proposed approach, a tool has been developed that can unify data from different CRM systems in a common format and make customer-centric interpretations of this data. The tool continuously monitors the systems it is integrated with, and when it detects the conditions defined by the users on the data, it can automatically perform the actions determined by the users. The tool developed was experienced by integrating a CRM system of a company and the evaluation results obtained in this process were discussed.

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Maintenance Automation Using Deep Learning Methods: A Case Study from the Aerospace Industry

Mayhew,

Ihshaish,

Deza

et al. 2023

Lecture Notes in Computer Science

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Customer feedbacks and opinion surveys analysis in the automotive industry

Cited by 4 publications

References 1 publication

Application of classification models on maintenance records through text mining approach in industrial environment

Application of classification models on maintenance records through text mining approach in industrial environment

An Ontology Based Approach for Next Generation Customer Relationship Management Systems

Maintenance Automation Using Deep Learning Methods: A Case Study from the Aerospace Industry

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