Mining affective experience for a kansei design study on a recliner

Kim, Wonjoon; Ko, Tae Sung; Rhiu, Ilsun; Yun, Myung Hwan

doi:10.1016/j.apergo.2018.08.014

Cited by 50 publications

(20 citation statements)

References 26 publications

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“…The frequency of negative words used in this cluster concludes that customers are highly concerned about the 'Ride' aspect of the service after driver's behavior. (19) Complaint (16) Poor (15) In summary, the negative words like 'Issue', 'Terrible', 'Horrible', 'Bad', and 'Worst' are present in all clusters with moderate to high frequency. However, the positive words like 'Good' and 'Better' have low to moderate frequency.…”

Section: B Frequency Analysis Of Clustersmentioning

confidence: 95%

“…Online reviews available on e-commerce web site and social media can solve the above-said problem [15]. Customer reviews available on these sites are increasing at a very fast pace due to rapid Internet growth [2].…”

Section: B Sentiment Analysismentioning

confidence: 99%

“…The researcher used the topic modeling approach and showed that people expressed negative attitude towards the websites that contain ads and positive attitude that do not . Similarly, online customer reviews are collected from popular e-commerce website i.e., Amazon and used to analyze the customer opinion about the "recliner" to carry out its Kansei engineering [15].…”

Section: B Sentiment Analysismentioning

confidence: 99%

“…[13]. As the reviews are enormous and unstructured, therefore, sentiment analysis is carried out to extract the sentiments conveyed by the customers about the quality of the product [14], [15]. These sentiments are then used as an input for Kansei engineering of product design and improvement, which enable the businesses to remain in market competition [16].…”

Section: Introductionmentioning

confidence: 99%

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Aspect Based Sentiment Analysis of Ridesharing Platform Reviews for Kansei Engineering

2020

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At present online reviews are becoming an important source for Kansei engineering of the services provided by ridesharing platforms. Kansei engineering deals with incorporating customer feedback and demands into product and service design. Thus, it is used as a tool for organizations to uplift their businesses by considering customer reviews and feedback. Customer reviews available on social media are in unstructured form; therefore, sentiment analysis is employed to extract customer's opinions in a systematic manner. In India-Pakistan, these reviews are mostly in Roman Urdu/Hindi and English, which are of great value for ridesharing platforms as a part of their Kansei engineering strategy. However, sentiment analysis cannot be performed directly on these reviews as they are mostly in Roman Urdu/Hindi. Therefore, the objective of this paper is to conduct aspect based sentiment analysis on these reviews after translating them into English for Kansei engineering of the service. Consequently, sentiment analysis is carried out to extract the most frequent features along with nouns and adjectives used by the customers to express their sentiments. We extracted prominent aspects of the service (i.e., 'Driver', 'Company', 'Service', and 'Ride') based on their highest frequencies using aspect based sentiment analysis. The customer sentiments are then clustered into these main aspects using unsupervised machine learning technique. Each aspect is further analyzed based on their polarity, which serves as an input for Kansei engineering of the service. As a result, it can facilitate ridesharing companies to enhance their businesses by improving services in accordance with customer demands.

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Section: B Frequency Analysis Of Clustersmentioning

confidence: 95%

Section: B Sentiment Analysismentioning

confidence: 99%

Section: B Sentiment Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Aspect Based Sentiment Analysis of Ridesharing Platform Reviews for Kansei Engineering

2020

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“…SIMEUNOVIC [5] studied how to apply emotional design methods in the development of new concepts for autonomous vehicles. KIM [6] proposed an emotional variable extraction and classification method combined with self-organizing maps to facilitate the collection and analysis of the user emotional experience. VIEIRA [7] et al explored the interface rubber keyboard design method that integrates functions and user experience through Kansei engineering methods.…”

Section: Introductionmentioning

confidence: 99%

Emotional Fusion and Design Optimization Method Based on Kansei-AD: A Case Study of Laser Additive Manufacturing Equipment

Mingyu

Jiang

Liu

et al. 2020

Preprint

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To improve the level of integration between the psychological and emotional needs of laser additive manufacturing equipment personnel and the cognitive interactive design of human-machine systems, an emotional optimization design method for laser additive manufacturing equipment based on Kansei engineering and axiomatic design (Kansei-AD) is proposed. By exploring the emotional cognitive process of personnel on equipment, the representation means of the psychological and emotional needs of personnel are clarified. To clarify the psychological and emotional needs of users, the Kansei semantic difference experiment is applied to determine the user domain of axiomatic design. To resolve the difficulty of accurately mapping and expressing between user emotional factors and design elements in the design process, a combination of rough analytic hierarchy process (rough AHP) and quality function deployment (QFD) is used in the functional domain to determine the user Kansei design needs. In addition, the mapping relationship between Kansei design requirements and design requirements is judged by independent axioms of axiomatic design. A matrix of morphological elements-design requirements is constructed, and the morphological design elements that meet the psychological and emotional needs of personnel are determined using the GRA-TOPSIS method. LDM4030 laser coaxial powder feeding equipment is taken as an example to verify the feasibility of the optimization method. The result shows that the method effectively optimizes the design of the equipment according to the psychological and emotional needs of personnel. It provides a new research idea for the psychological and emotional design of laser additive manufacturing equipment.

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Whether Information Source Should Be Provided in the Response of Voice Interaction System?

Ya-ping

Zhou

Sun

et al. 2020

Lecture Notes in Computer Science

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Mining affective experience for a kansei design study on a recliner

Cited by 50 publications

References 26 publications

Aspect Based Sentiment Analysis of Ridesharing Platform Reviews for Kansei Engineering

Aspect Based Sentiment Analysis of Ridesharing Platform Reviews for Kansei Engineering

Emotional Fusion and Design Optimization Method Based on Kansei-AD: A Case Study of Laser Additive Manufacturing Equipment

Whether Information Source Should Be Provided in the Response of Voice Interaction System?

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