A Text Mining Approach for Automatic Modeling of Kansei Evaluation from Review Texts

Yamada, Atsuhiro; Hashimoto, Sho; Nagata, Noriko

doi:10.1007/978-981-10-8612-0_34

Cited by 8 publications

(4 citation statements)

References 6 publications

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“…This research has used the Kansei approach to study visual content and investigate the evoked emotions in extremist YouTube videos among younger viewers [ 133 ].The method help in finding the specific emotion regarding content on the online social platform, but it does not involve finding any score of emotion that can help enhance the accuracy of the emotion classification. Different from this, researchers use the Kansei approach to construct the Kansei evaluation model for analysing product design from product reviews on the web by applying NLP methods based on the business/product domain [ 134 ]. From those methods, it can calculate and recognize the related scores evaluated by subjective experiments.…”

Section: Discussionmentioning

confidence: 99%

“…This is challenging because Kansei is still a new approach and has limitations such as the lack of a systematic method for assigning scores to entities for emotion evaluation experiments in research. In 2018, Yamada et al [ 134 ] implemented a text mining technique to perform Kansei evaluation for a product design. They found that the method is useful, and it is in automatic form.…”

Section: Discussionmentioning

confidence: 99%

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Opinion mining for national security: techniques, domain applications, challenges and research opportunities

et al. 2021

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Background Opinion mining, or sentiment analysis, is a field in Natural Language Processing (NLP). It extracts people’s thoughts, including assessments, attitudes, and emotions toward individuals, topics, and events. The task is technically challenging but incredibly useful. With the explosive growth of the digital platform in cyberspace, such as blogs and social networks, individuals and organisations are increasingly utilising public opinion for their decision-making. In recent years, significant research concerning mining people’s sentiments based on text in cyberspace using opinion mining has been explored. Researchers have applied numerous opinions mining techniques, including machine learning and lexicon-based approach to analyse and classify people’s sentiments based on a text and discuss the existing gap. Thus, it creates a research opportunity for other researchers to investigate and propose improved methods and new domain applications to fill the gap. Methods In this paper, a structured literature review has been done by considering 122 articles to examine all relevant research accomplished in the field of opinion mining application and the suggested Kansei approach to solve the challenges that occur in mining sentiments based on text in cyberspace. Five different platforms database were systematically searched between 2015 and 2021: ACM (Association for Computing Machinery), IEEE (Advancing Technology for Humanity), SCIENCE DIRECT, SpringerLink, and SCOPUS. Results This study analyses various techniques of opinion mining as well as the Kansei approach that will help to enhance techniques in mining people’s sentiment and emotion in cyberspace. Most of the study addressed methods including machine learning, lexicon-based approach, hybrid approach, and Kansei approach in mining the sentiment and emotion based on text. The possible societal impacts of the current opinion mining technique, including machine learning and the Kansei approach, along with major trends and challenges, are highlighted. Conclusion Various applications of opinion mining techniques in mining people’s sentiment and emotion according to the objective of the research, used method, dataset, summarized in this study. This study serves as a theoretical analysis of the opinion mining method complemented by the Kansei approach in classifying people’s sentiments based on text in cyberspace. Kansei approach can measure people’s impressions using artefacts based on senses including sight, feeling and cognition reported precise results for the assessment of human emotion. Therefore, this research suggests that the Kansei approach should be a complementary factor including in the development of a dictionary focusing on emotion in the national security domain. Also, this theoretical analysis will act as a reference to researchers regarding the Kansei approach as one of the techniques to improve hybrid approaches in opinion mining.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Opinion mining for national security: techniques, domain applications, challenges and research opportunities

et al. 2021

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“…This method quantifies the relationship between service properties, perceived responses, and usage intentions through data mining techniques utilizing decision trees. Yamada, Hashimoto, and Nagata (2018) have proposed a method that automatically builds a Kansei evaluation model for product design using review texts on the web. This method consists of three steps.…”

Section: Kansei Mining Process and Methodologymentioning

confidence: 99%

“…In the last two decades, several studies have been conducted on applying KE in designing and developing new products and services and reviewing and evaluating product and service designs in offline and online formats. Some research applications of KE and association rules for mining in product design (Pitaktiratham 2012), such as mobile phone affective design (Jiao, Zhang, and Helander 2006;Fuqian, Shouqian, and Jiang 2007;Shi, Sun, and Xu 2012;Fung et al 2012;and Oztekin et al 2013), Kanazawa gold leaf, a traditional craft in Ishikawa, Japan (Yan et al 2008), truck cab (Yang et al 2008), truck cab interior design (Zhou et al 2010), interface design of e-commerce web for 3 C products (Wang, Wang, and Chen 2010), clothing design (Xiaoxi, Hui' e, and Zhiya 2017), the packaging design of powder shaped fresheners (Djatna and Kurniati 2015), shoe product (Li et al 2017), office chair design (Kobayashi and Kinumura 2017), wristwatches (Yamada, Hashimoto, and Nagata 2018), road bike (Chiu and Lin 2018), ceramic (Kittidecha and Yamada 2018), recliners (Kim et al 2019), computer mouse (Jiao and Qu 2019), mini digital camera (Guo et al 2014), and digital camera design (Ali et al 2020).…”

Section: Introductionmentioning

confidence: 99%

Kansei engineering with online review mining methodology for robust service design

Hartono,

Prayogo,

Ronyastra

et al. 2023

Theoretical Issues in Ergonomics Science

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Kansei Engineering (KE) has shown its prominent applicability in service design and development, focusing on translating and interpreting customers' emotional needs (Kansei) into service characteristics. It is critical and promising as the services sector has grown faster than the manufacturing sector in developing economies in the past three decades. It accounted for an average of 55% of GDP in some developing economies. KE's flexibility in collaborating with other methods and covering various service settings shows its unique superiority. However, there is criticism of the collected Kansei's validity and the proposed solution's robustness. It might be potentially caused by the dynamics of customer emotional needs and various service settings. As a result, Kansei is found to be somewhat fuzzy, unclear, and ambiguous. Hence, a more structured KE methodology incorporating the Kansei text mining process for robust service design is proposed. Kansei text mining approach will extract and summarize service attributes and their corresponding affective responses based on the online product descriptions and customer reviews. The Taguchi method will support the robustness of the proposed improvement strategy. An empirical study of a zoo as a tourism attraction service and its practical implication is discussed and validated in the proposed integrative framework. Relevance statementThis study is relevant to human factors engineering in terms of the role of affect or Kansei incorporating online review mining methodology. Through a focus on attractive qualities of service attributes and robust improvement strategy, this study enables service providers, managers, and practitioners to establish the extent to which they prioritise their continuous improvement strategies, aiming customer delight beyond satisfaction.

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A Case Study on Turkish Tea Glasses and Kansei Engineering

Göken

Alppay

2019

Advances in Intelligent Systems and Computing

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A Text Mining Approach for Automatic Modeling of Kansei Evaluation from Review Texts

Cited by 8 publications

References 6 publications

Opinion mining for national security: techniques, domain applications, challenges and research opportunities

Opinion mining for national security: techniques, domain applications, challenges and research opportunities

Kansei engineering with online review mining methodology for robust service design

A Case Study on Turkish Tea Glasses and Kansei Engineering

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