Jian Jin

Bio: Jian Jin is an academic researcher from Beijing Normal University. The author has contributed to research in topics: Product design & Sentiment analysis. The author has an hindex of 20, co-authored 56 publications receiving 1176 citations. Previous affiliations of Jian Jin include Xi'an Jiaotong University & Hong Kong Polytechnic University.

Identifying helpful online reviews: A product designer's perspective

Abstract: Large amounts of online reviews, the valuable voice of the customer, benefit consumers and product designers. Identifying and analyzing helpful reviews efficiently and accurately to satisfy both current and potential customers' needs have become a critical challenge for market-driven product design. Existing evaluation methods only use the review voting ratios given by customers to measure helpfulness. Due to the issues such as viewpoints of interest, technical proficiency and domain knowledge involved, it may mislead designers in identifying those truly valuable and insightful opinions from designers' perspective. Thus, in this study, we initiate our work to explore a possible approach that bridges the opinions expressed by consumers and the understanding gathered by designers in terms of how helpful these opinions are. Our ultimate research focus is on how to automatically evaluate the helpfulness of an online review from a designer's viewpoint entirely based on the review content itself. We start our work by first conducting an exploratory study to understand the fundamental question of what makes an online customer review helpful from product designers' viewpoint. Through our study, we propose four categories of features that reflect designers' concerns in judging review helpfulness. Based on our experiments, it reveals that discrepancy does exist between both online customer voting and designers' rating. Furthermore, for the cases where review ratings are not steadily available, we have proposed to use regression to predict and interpret review helpfulness with the help of the aforementioned four categories of features that are entirely extracted from review content. Finally, using review data crawled from Amazon.com and real ratings given by design personnel, it demonstrates the effectiveness of our proposal and it also suggests that helpful product reviews can be identified from a designer's angle by automatically analyzing the review content. We argue that the study reported is able to improve designer's ability in business intelligence processing by offering more targeted customer opinions. It highlights the urgency to uncover sensible user requirements from such quality opinions in our future research.

Understanding big consumer opinion data for market-driven product design

Abstract: Big consumer data provide new opportunities for business administrators to explore the value to fulfil customer requirements (CRs). Generally, they are presented as purchase records, online behaviour, etc. However, distinctive characteristics of big data, Volume, Variety, Velocity and Value or ‘4Vs’, lead to many conventional methods for customer understanding potentially fail to handle such data. A visible research gap with practical significance is to develop a framework to deal with big consumer data for CRs understanding. Accordingly, a research study is conducted to exploit the value of these data in the perspective of product designers. It starts with the identification of product features and sentiment polarities from big consumer opinion data. A Kalman filter method is then employed to forecast the trends of CRs and a Bayesian method is proposed to compare products. The objective is to help designers to understand the changes of CRs and their competitive advantages. Finally, using opinion data in Amazon.com, a case study is presented to illustrate how the proposed techniques are applied. This research is argued to incorporate an interdisciplinary collaboration between computer science and engineering design. It aims to facilitate designers by exploiting valuable information from big consumer data for market-driven product design.

Dielectric and piezoelectric properties of [0 0 1] and [0 1 1]-poled relaxor ferroelectric PZN-PT and PMN-PT single crystals

Abstract: The properties of PZN–PT and PMN–PT single crystals of varying compositions and orientations have been investigated. Among the various compositions studied, [0 0 1]-optimally poled PZN-(6–7)%PT and PMN-30%PT exhibit superior dielectric and piezoelectric properties, with K T ≈ 6800–8000, d 33 ≈ 2800 pC/N, d 31 ≈ −(1200–1800) pC/N for PZN-(6–7)%PT; and K T = 7500–9000, d 33 = 2200–2500 pC/N and d 31 = −(1100–1400) pC/N for PMN-30%PT. These two compositions are also fairly resistant to over-poling. The [0 0 1]-poled electromechanical coupling factors ( k 33 , k 31 and k t ) are relatively insensitive to crystal composition. [0 1 1]-optimally poled PZN-7%PT single crystal also exhibits extremely high d 31 values of up to −4000 pC/N with k 31 ≈ 0.90–0.96. While [0 1 1]-poled PZN-7%PT single crystal becomes over-poled with much degraded properties when poled at and above 0.6 kV/mm, PZN-6%PT crystal shows no signs of over-poling even when poled to 2.0 kV/mm. The presence of a certain amount (i.e., 10–15%) of orthorhombic phase in a rhombohedral matrix has been found to be responsible for the superior transverse piezoelectric properties of [0 1 1]-optimally poled PZN-(6–7)%PT. The present work shows that flux-grown PZN–PT crystals exhibit superior and consistent properties and improved over-poling resistance to flux-grown PMN–PT crystals and that, for or a given crystal composition, flux-grown PMN–PT crystals exhibit superior over-poling resistance to their melt-grown counterparts.

Quantification and integration of Kano’s model into QFD for optimising product design

Abstract: With increasing concerns on customer needs in today’s competitive market, the issue of incorporating customer requirements into product design arises the interest of both researchers and practitioners. Quality Function Deployment (QFD) is a well-known methodology for customer-driven product design. However, conventionally, QFD analysis has a major challenge in understanding customer needs accurately. Kano’s model, which studies the nature of customer needs, provides a way for a better classification of customer needs. However, seldom research contributions are found in terms of integrating Kano’s model with QFD quantitatively. In this research, a novel integration approach is proposed. At first, Kano’s model is quantified by identifying relationship between customer needs and customer satisfaction (CS). Next, both qualitative and quantitative results from Kano’s model are integrated into QFD. Finally, a mixed non-linear integer programming model is formulated to maximise CS under cost and technical constr...

A survey on opinion mining and sentiment analysis

Abstract: With the advent of Web 2.0, people became more eager to express and share their opinions on web regarding day-to-day activities and global issues as well. Evolution of social media has also contributed immensely to these activities, thereby providing us a transparent platform to share views across the world. These electronic Word of Mouth (eWOM) statements expressed on the web are much prevalent in business and service industry to enable customer to share his/her point of view. In the last one and half decades, research communities, academia, public and service industries are working rigorously on sentiment analysis, also known as, opinion mining, to extract and analyze public mood and views. In this regard, this paper presents a rigorous survey on sentiment analysis, which portrays views presented by over one hundred articles published in the last decade regarding necessary tasks, approaches, and applications of sentiment analysis. Several sub-tasks need to be performed for sentiment analysis which in turn can be accomplished using various approaches and techniques. This survey covering published literature during 2002-2015, is organized on the basis of sub-tasks to be performed, machine learning and natural language processing techniques used and applications of sentiment analysis. The paper also presents open issues and along with a summary table of a hundred and sixty-one articles.

Digital Twin and Big Data Towards Smart Manufacturing and Industry 4.0: 360 Degree Comparison

Abstract: With the advances in new-generation information technologies, especially big data and digital twin, smart manufacturing is becoming the focus of global manufacturing transformation and upgrading. Intelligence comes from data. Integrated analysis for the manufacturing big data is beneficial to all aspects of manufacturing. Besides, the digital twin paves a way for the cyber-physical integration of manufacturing, which is an important bottleneck to achieve smart manufacturing. In this paper, the big data and digital twin in manufacturing are reviewed, including their concept as well as their applications in product design, production planning, manufacturing, and predictive maintenance. On this basis, the similarities and differences between big data and digital twin are compared from the general and data perspectives. Since the big data and digital twin can be complementary, how they can be integrated to promote smart manufacturing are discussed.

Digital twin-driven product design framework

Abstract: With the advent of new generation information technologies in industry and product design, the big data-driven product design era has arrived. However, the big data-driven product design mainly pla...