Towards a kansei-based user modeling methodology for eco-design

We propose here to highlight the benefits of building a framework linking Kansei Design (KD), User Centered Design (UCD) and Eco-design, as the correlation between these fields is barely explored in research at the current time. Therefore, we believe Kansei Design could serve the goal of achieving more sustainable products by setting up an accurate understanding of the user in terms of ecological awareness, and consequently enhancing performance in the Eco-design process. In the same way, we will consider the means-end chain approach inspired from marketing research, as it is useful for identifying ecological values, mapping associated functions and defining suitable design solutions. Information gathered will serve as entry data for conducting scenario-based design, and supporting the development of an Eco-friendly User Centered Design methodology (EcoUCD).ANR-ECOUS

Kansei Mining-based In Services sebagai Alternatif Pengembangan Metodologi Affective Design

—Beberapa penelitian di bidang desain afektif atau dikenal dengan Kansei engineering for affective design dihadapkan pada tantangan dan peluang untuk mendapatkan kebutuhan emosional (disebut juga sebagai Kansei) yang valid dan benar-benar representatif terhadap kebutuhan dan pengalaman konsumen terhadap produk atau layanan tertentu dalam masa tertentu. Kansei memang sensitif terhadap obyek, kultur, waktu dan juga dinamika dari kebutuhan emosional pelanggan itu sendiri. Tantangan yang ada selama ini adalah memetakan hubungan secara benar dan representatif antara Kansei dan service attributes. Seringkali yang terjadi adalah adanya lacking dari proses pengumpulan dan validasi Kansei words yang terbatas pada jumlah sampel dan metodologinya. Dengan demikian, studi ini dilakukan dengan menitikberatkan pada studi literatur dan pengembangan model Kansei engineering menggunakan pendekatan text mining di industri jasa. Kata kunci: Affective design, Kansei engineering, layanan, text minin

Kansei Mining-based in Services sebagai Alternatif Pengembangan Metodologi Affective Design

Abstract—Recent research in the field of affective design or known as Kansei engineering for affective design is faced with challenges and opportunities to obtain emotional needs (also called Kansei) that are valid and truly representative of the needs and experience of customer for certain products or services in a certain period. Kansei is indeed sensitive to objects, culture, time-based and the changes of the customer's emotional needs. The challenge so far has been to map the relationship between Kansei and service attributes correctly and representatively. Often what happens is that lack of the process of collecting and validating Kansei words which is limited to the number of samples and research methodology. Thus, this study was carried out by focusing on the study of literature and the development of Kansei engineering model using Kansei text-based mining approach applied to services. Keywords: Affective design, Kansei engineering, services, text mining Abstrak—Beberapa penelitian di bidang desain afektif atau dikenal dengan Kansei engineering for affective design dihadapkan pada tantangan dan peluang untuk mendapatkan kebutuhan emosional (disebut juga sebagai Kansei) yang valid dan benar-benar representatif terhadap kebutuhan dan pengalaman konsumen terhadap produk atau layanan tertentu dalam masa tertentu. Kansei memang sensitif terhadap obyek, kultur, waktu dan juga dinamika dari kebutuhan emosional pelanggan itu sendiri. Tantangan yang ada selama ini adalah memetakan hubungan secara benar dan representatif antara Kansei dan service attributes. Seringkali yang terjadi adalah adanya lacking dari proses pengumpulan dan validasi Kansei words yang terbatas pada jumlah sampel dan metodologinya. Dengan demikian, studi ini dilakukan dengan menitikberatkan pada studi literatur dan pengembangan model Kansei engineering menggunakan pendekatan text mining di industri jasa. Kata kunci: Affective design, Kansei engineering, layanan, text minin

Text analytics on MOOCs. A comprehensive analysis of emotions

The value of diversity in education is highly emphasized in recent years, particularly in the wake of the COVID-19 pandemic, by many scholars. Massive open online courses (MOOCs) have aided the evolution of online learning by broadening the range of learning opportunities available. They have gained popularity, especially in higher education by providing unlimited access to lectures and rich learning materials by renowned and respected academics in a wide variety of areas, with no restrictions and at very low fees. Furthermore, learners' motivations for enrolling in a MOOC may vary depending on their choices for the course's instructional design as well as their emotions. Knowing this, the development of more effective online courses that address affective concerns would appeal to a wider audience and improve the learning experience. This research aims to uncover the emotional characteristics of MOOCs to better understand why learners choose a specific course among hundreds of options available on MOOC sites. For extracting the learners' emotions from user reviews, the study used Kansei Engineering approach, which is enhanced with text analytics techniques. The research methodology entails gathering reviews from MOOCs and analyzing them using natural language processing (NLP) techniques to discover Kansei words that characterize MOOCs, notably for courses in the discipline of Data Science. The expected output of this study is a Kansei corpus for online courses in this discipline

Exploring the KEER knowledge landscape over the past decade

The aim of this paper was to systematically explore the knowledge landscape of papers presented at KEER conferences over the last decade. We collected all papers published in conference proceedings between 2010 and 2020. We (i) used a text mining pipeline to extract, clean, and normalize keywords from the Title and Abstract fields, and (ii) created a co-occurrence network reflecting the relationships between keywords. The network was then characterized at different levels of granularity (static analysis vs. time slice analysis and whole network vs. node-level analysis). The exploratory analysis showed a stable expansion of the network over time. The cluster structure revealed several groups of keywords that did not change over time and reflected both domain-specific and method-specific topics of research in Kansei engineering

Natural Language Processing in-and-for Design Research

We review the scholarly contributions that utilise Natural Language Processing (NLP) methods to support the design process. Using a heuristic approach, we collected 223 articles published in 32 journals and within the period 1991-present. We present state-of-the-art NLP in-and-for design research by reviewing these articles according to the type of natural language text sources: internal reports, design concepts, discourse transcripts, technical publications, consumer opinions, and others. Upon summarizing and identifying the gaps in these contributions, we utilise an existing design innovation framework to identify the applications that are currently being supported by NLP. We then propose a few methodological and theoretical directions for future NLP in-and-for design research

Case-based reasoning for product style construction and fuzzy analytic hierarchy process evaluation modeling using consumers linguistic variables

Key form features are relative to the style of a product and the expression style features depict the product description and are a measurement of attribute knowledge. The uncertainty definition leads to an improved and effective product style retrieval when combined with fuzzy sets. Firstly, a style knowledge and features database are constructed using fuzzy case based reasoning technology (FCBR). A similarity measurement method based on case-based reasoning and fuzzy model of the fuzzy proximity method may be defined by the Fuzzy Nearest-Neighbor (FNN) algorithm obtaining the style knowledge extraction. Secondly, the Linguistic Variables (LV) are used to assess the product characteristics to establish the product style evaluation database for simplifying the style presentation and decreasing the computational complexity. Thirdly, the model of product style feature set, extracted by FAHP and the final style related form features set, are acquired using LV. This research involves a case study for extracting the key form features of the style of high heel shoes. The proposed algorithms are generated by calculating the weights of each component of high heel shoes using FAHP with LV. The case study and results established that the proposed method is feasible and effective for extracting the style of the product

Extreme learning machine classification of file clusters for evaluating content-based feature vectors

In the digital forensic investigation and missing data files retrieval in general, there is a challenge of recovering files that have missing system information. The recovery process entails applying a number of methods to determine the type, the contents and the structure of each data file clusters such as JPEG, DOC, ZIP or TXT. This paper studies the effects of three content-based features extraction methods in improving the classification of JPEG File clusters. The methods are Byte Frequency Distribution, Entropy, and Rate of Change. Consequently, an Extreme Learning Machine (ELM) neural network algorithm is used to evaluate the performance of the three methods in which it classifies the class label of the feature vectors to JPEG and Non-JPEG images for files in different file formats. The files are allocated in a continuous series of clusters. The ELM algorithm is applied to the DFRWS (2006) dataset and the results show that the combination of the three methods produces 93.46% classification accuracy