No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Exploring the integration of social media feedback for user-oriented product development
Abstract
Product designers thrive on designing products to fulfil various expectations and needs from customers. To understand the customer expectation and needs, it is crucial to have the information on customer feedback that is generated during product usage phase. For this purpose, social media has attracted strong interest, as increasing amount of information is published daily by customers. This information is related to a wide range of products and contains product specific feedbacks. To make use of the feedbacks, different approaches were developed and described in literature. Most of them focused on the extraction of limited information to support specific tasks, which is however not flexible and general enough. Little research has provided a practicable and flexible solution to support different design tasks in various domains. This article suggests a social media wrapper approach, which can be flexibly configured to address this issue. It provides designers a holistic view of the feedbacks that widely distributed in different social media channels as well as in diversity data sources. This holistic view of feedbacks can be analyzed to earn necessary knowledge for design tasks.
... User review data on online review sites, such as Amazon, were often analysed to elicit user preferences (Joung and Kim, 2021) and user needs (Han and Moghaddam, 2021;Shi et al., 2017). Data on patent databases were also used to create creative stimuli through analogical inspiration (Jiang et al., 2022;Song et al., 2020) and product function recommendations (Deng et al., 2017). ...
This study provides a systematic literature review, investigating current uses and application of behavioural data in services design process. The results show a predominance of data usage either on product design process by professional designers or on personal reflection by service users, and more importantly, there is a large gap in what designers and users can benefit from data. The paper argues that such a gap limits the potentials of data in service design and highlights the importance of co-design between designers and users within data-driven design.
... For the integration, this application scenario used the Semantic Mediator, i.e., the social media wrapper [47]. With knowledge from the previous steps, the social media wrapper is configured to extract information from identified fields in the customer call logs for satisfying the defined information needs. ...
Product design is crucial for product success. Many approaches can improve product design quality, such as concurrent engineering and design for X. This study focuses on applying product usage information (PUI) during product development. As emerging technologies become widespread, an enormous amount of product-related information is available in the middle of a product’s life, such as customer reviews, condition monitoring, and maintenance data. In recent years, the literature describes the application of data analytics technologies such as machine learning to promote the integration of PUI during product development. However, as of today, PUI is not efficiently exploited in product development. One of the critical issues to achieve this is identifying and integrating task-relevant PUI fit for purposes of different product development tasks. Nevertheless, preparing task-relevant PUI that fits different product development tasks is often ignored. This study addresses this research gap in preparing task-relevant PUI and rectifies the related shortcomings and challenges. By considering the context in which PUI is utilized, this paper presents a systematic procedure to help identify and specify developers’ information needs and propose relevant PUI fitting the actual information needs of their current product development task. We capitalize on an application scenario to demonstrate the applicability of the proposed approach.
... In recent years, there has been an increasing interest in modeling these information flows ( Deng, et al., 2017). However, the PUI using flows, are still incomplete and need more investigations. ...
Improving the management of product lifecycle is becoming increasingly important today. The concept of Closed Loop Product Lifecycle Management (CL-PLM) has been developed to help organizations in the value chain to promote the comprehensive management of product lifecycle information and activities. CL-PLM attempts to manage all processes related to products, from product design to end of product life. However, the development of this concept is not still complete. There is a need for more research to manage the data and information flows and to provide each organization in the value chain with the right data from product Middle Of the Life (MOL). In this thesis, a new approach is presented, which completes the concept of CL-PLM in terms of information requirements of product beneficiaries in MOL of the product. The research question is as follows: Which groups and organizations use the products or are affected by it and can be benefited from the product's operational information? The research involves the determination of the information needs of the stakeholders and tests the automatic provision of information to stakeholder organizations (beneficiaries) using data analytics methods. The methodology used in this thesis is an inductive approach. To this end, two case studies on engineered products are carried out. Based on these two studies, the stakeholders involved in MOL, which benefit from the product and its information, are identified. Next, the dissertation identifies current and future information needs. Interviews and surveys have been used to identify information that MOL stakeholders need. The second part of this thesis examines, which data analytics methods can be useful for supporting each information need. After the completion of analysis, the findings are tested and implemented in real-world scenarios. The scenarios show how these information requirements can be satisfied with data analytics. The findings of this research are various. First is to find out the MOL stakeholders of the product lifecycle and grouping the most influential ones, which are affected by product MOL data and information. Moreover, identifying their current and potential future information needs from the product MOL. Second is a classification of data analytics tools (suggestions on suitable tools) that can help the stakeholders to meet their new information needs from the product, or improve their access to product MOL information. The third part of the results is the implementation of the mentioned concept in three scenarios. In implementation part, information needs of OEM about the performance of a component in an electric vehicle, information need of maintenance provider from motorboat in respect of spare part supply and information need of wind farm operator from windmills for contact design are selected and modeled. The latter shows that it is possible to automatize the provision of the required information to the stakeholders. These three contributions form the output of this dissertation. In sum, the findings of this thesis can be helpful for product and service manufacturers, especially those who want to move from pure production to the production of a product with the services, and organizations, who wish to add intelligence to their products. This research can also be useful for PLM software developers. Finally, it is helpful for all companies involved in the realization of engineered products that want to understand and manage the lifecycle of the product.
... . Beiträge in sozialen Medien können dahingehend ausgewertet werden, welche Erfahrungen Kunden mit Produkten machen, was sie für Verbesserungswünsche haben oder welche Probleme sie erleben [7]. Die Kombination dieser Informationen stellt für die Unternehmen ein wertvolles Gut dar. ...
Produktnutzungsinformationen geben Aufschluss über den tatsächlichen Gebrauch eines individuellen Produktes. Sie werden in der Nutzungsphase des Produktlebenszyklus generiert, die Nutzung, Wartung und Service umfasst. Bislang war eine Sammlung dieser Informationen weitestgehend auf wartungs- und servicebezogenen Quellen beschränkt. Heute aber erlaubt die Digitalisierung von Produkten einen direkten Einblick in das tatsächliche Nutzungsverhalten von Kunden. Zudem lassen sich Beiträge von Kunden in sozialen Medien und ähnlichen Internetquellen hinsichtlich ihrer produktbezogenen Anforderungen, Wünsche und Probleme auswerten. Diese Informationen lassen sich systematisch in die Entwicklung neuer bzw. in die Verbesserung existierender Produkte oder produktbegleitender Dienstleistungen einbringen. Allerdings setzt ein effizienter Einsatz von Produktnutzungsinformationen ihre semantische Interoperabilität mit den Prozessen und unterstützenden IT-Systemen voraus. Dieser Beitrag präsentiert einen Ansatz und seine Umsetzung als IT-Plattform, der diese Voraussetzung schafft.
Product development insights may be found through user reviews on App stores, product forums, and social media. This feedback is often regarded as the “voice of the users”. This feedback has been subject to a lot of recent research, intending to create systems that can automatically extract, filter, analyze, and report the concerned feedback data in near real time. As per our survey results, often this user feedbacks do not reach the concerned organization promptly due to the volume, veracity, and velocity of feedback from multiple channels. In this rese arch work, we propose using sentiment analysis and social media mining an automatic engine which can be used for better product recommendation and automatic routing of relevant feedback to the product development teams. Our proposed solution is scheduled to run at regular intervals pulling dynamic reviews in an optimized manner with a lesser time complexity and higher efficiency. The reviews are collated from distributed platforms followed by building a domain classification engine on the principles of TF-IDF and Supervised Classifier. This system is used to classify the reviews of the respective enterprises. A sentiment analysis system is built using combined Rule-Based Mining and Supervised Learning Models which makes use of polarity to classify if the feedback is positive or negative. If the polarity is negative, the feedback gets routed to the concerned enterprise for immediate action and if the polarity is positive, it is passed to a user-based collaborative filtering engine which acts as a recommendation system.
A product's value is increasingly determined by the services supporting it. Complex Product-Service Systems (PSS) – combinations of services and products – are in demand. Product usage information (PUI), that is, information about how an individual product is used, is rapidly becoming a valuable asset to industry to help inform service offers throughout the product lifecycle. Conventionally, information about how a product is used is gathered in customer relations and MRO (maintenance, repair and overhaul) processes via channels such as repair logs, call centers or helpdesks. Today, new channels of PUI are emerging from the digitalization of products, the Internet of Things, Cyber-Physical Systems, Internet sources and social media. To benefit from the user experience and knowledge about product-service usage contained in PUI, current product and service development systems (e.g. PDM/PLM, CAx and simulation tools) and the related processes and methodologies need to be extended to incorporate PUI feedback and feedforward loops. This means a suitable IT infrastructure is required to efficiently and seamlessly integrate and apply PUI to PSS (re-)design processes. This paper proposes such an IT infrastructure in the form of an architecture for a Virtual Open Platform for incorporating feedback loops into PSS (re-)design processes and the corresponding IT systems.
In product design and engineering, identifying customer needs is the foundation for designing and producing a successful product. Traditionally, a range of techniques have been employed to elicit customer needs. A relatively new technique for identifying customer needs is ‘crowdsourcing’. An emerging area of research is the crowdsourcing of customer needs from online product review sites. This paper proposes a simple process for crowdsourcing customer needs for product design using text analytics. The analysis/visualization method is presented in detail. The text content of online customer reviews for a popular product is collected and processed using text analytics software. A published case study identifying expressed customer needs for the same generic product, collected via conventional means, is used to successfully validate the findings from the text analytics method.
The field of sentiment analysis, in which sentiment is gathered, analyzed, and aggregated from text, has seen a lot of attention in the last few years. The corresponding growth of the field has resulted in the emergence of various subareas, each addressing a different level of analysis or research question. This survey focuses on aspect-level sentiment analysis, where the goal is to find and aggregate sentiment on entities mentioned within documents or aspects of them. An in-depth overview of the current state-of-the-art is given, showing the tremendous progress that has already been made in finding both the target, which can be an entity as such, or some aspect of it, and the corresponding sentiment. Aspect-level sentiment analysis yields very fine-grained sentiment information which can be useful for applications in various domains. Current solutions are categorized based on whether they provide a method for aspect detection, sentiment analysis, or both. Furthermore, a breakdown based on the type of algorithm used is provided. For each discussed study, the reported performance is included. To facilitate the quantitative evaluation of the various proposed methods, a call is made for the standardization of the evaluation methodology that includes the use of shared data sets. Semantically-rich concept-centric aspect-level sentiment analysis is discussed and identified as one of the most promising future research direction.
Different from explicit customer needs that can be identified directly by analyzing raw data from the customers, latent customer needs are often implied in the semantics of use cases underlying customer needs information. Due to difficulties in understanding semantic implications associated with use cases, typical text mining-based methods can hardly identify latent customer needs, as opposite to keywords mining for explicit customer needs. This paper proposes a two-layer model for latent customer needs elicitation through use case reasoning. The first layer emphasizes sentiment analysis, aiming to identify explicit customer needs based on the product attributes and ordinary use cases extracted from online product reviews. Fuzzy support vector machines (SVMs) are developed to build sentiment prediction models based on a list of affective lexicons. The second layer is geared toward use case analogical reasoning, to identify implicit characteristics of latent customer needs by reasoning the semantic similarities and differences analogically between the ordinary and extraordinary use cases. Case-based reasoning (CBR) is utilized to perform case retrieval and case adaptation. A case study of Kindle Fire HD 7 in. tablet is developed to illustrate the potential and feasibility of the proposed method.
WITH MORE THAN 4,100 properties in more than 90 countries, Accor Hospitality was facing pressure from customers, as well as from shareholders, to increase customer satisfaction and quality of service during an economic downturn. It thus turned to Synthesio, a global, multilingual social-media monitoring-andresearch company, to examine the more than 5,000 customer opinions posted each month on travel websites worldwide concerning Accor's various brands. Accor saw its main challenge as how to quickly identify customer dissatisfaction and then correct problems at their source. Synthesio created a tool to track the online reputations of 12,000 hotels, including those run by Accor and those run by its competitors. It quickly revealed a number of problems Accor guests were having; for example, room keys were being demagnetized unintentionally by their smartphones. Accor was then able to work with its room-key supplier to address the problem. Accor was also able to set up.
Traditionally, consumers used the Internet to simply expend content: they read it, they watched it, and they used it to buy products and services. Increasingly, however, consumers are utilizing platforms--such as content sharing sites, blogs, social networking, and wikis--to create, modify, share, and discuss Internet content. This represents the social media phenomenon, which can now significantly impact a firm's reputation, sales, and even survival. Yet, many executives eschew or ignore this form of media because they don't understand what it is, the various forms it can take, and how to engage with it and learn. In response, we present a framework that defines social media by using seven functional building blocks: identity, conversations, sharing, presence, relationships, reputation, and groups. As different social media activities are defined by the extent to which they focus on some or all of these blocks, we explain the implications that each block can have for how firms should engage with social media. To conclude, we present a number of recommendations regarding how firms should develop strategies for monitoring, understanding, and responding to different social media activities.
In this paper, we discuss the use of ontologies for semantic interoperability and integration. We argue that information technology has evolved into a world of largely loosely coupled systems and as such, needs increasingly more explicit, machine-interpretable semantics. Ontologies in the form of logical domain theories and their knowledge bases offer the richest representations of machine-interpretable semantics for systems and databases in the loosely coupled world, thus ensuring greater semantic interoperability and integration. Finally, we discuss how ontologies support semantic interoperability in the real, commercial and governmental world.
Information Extraction aims to retrieve certain types of information from natural language text by processing them automatically. For example, an information extraction system might retrieve information about geopolitical indicators of countries from a set of web pages while ignoring other types of information. Ontology-based information extraction has recently emerged as a subfield of information extraction. Here, ontologies - which provide formal and explicit specifications of conceptualizations - play a crucial role in the information extraction process. Because of the use of ontologies, this field is related to knowledge representation and has the potential to assist the development of the Semantic Web. In this paper, we provide an introduction to ontology-based information extraction and review the details of different ontology-based information extraction systems developed so far. We attempt to identify a common architecture among these systems and classify them based on different factors, which leads to a better understanding on their operation. We also discuss the implementation details of these systems including the tools used by them and the metrics used to measure their performance. In addition, we attempt to identify the possible future directions for this field.
With the rapid growth of social media, sentiment analysis, also called opinion mining, has become one of the most active research areas in natural language processing. Its application is also widespread, from business services to political campaigns. This article gives an introduction to this important area and presents some recent developments.
Financial news plays a significant role with regard to predicting the behaviour of financial markets. However, the exponential growth of financial news on the Web has led to a need for new technologies that automatically collect and categorise large volumes of information in a fast and easy manner. Sentiment analysis, or opinion mining, is the field of study that analyses people’s opinions, moods and evaluations using written text on Web platforms. In recent research, a substantial effort has been made to develop sophisticated methods with which to classify sentiments in the financial domain. However, there is a lack of approaches that analyse the positive or negative orientation of each aspect contained in a document. In this respect, we propose a new sentiment analysis method for feature and news polarity classification. The method presented is based on an ontology-driven approach that makes it possible to semantically describe relations between concepts in the financial news domain. The polarity of the features in each document is also calculated by taking into account the words from around the linguistic expression of the feature. These words are obtained by using the ‘N_GRAM After’, ‘N_GRAM Before’, ‘N_GRAM Around’ and ‘All_Phrase’ methods. The effectiveness of our method has been proved by carrying out a set of experiments on a corpus of 1000 financial news items. Our proposal obtained encouraging results with an accuracy of 66.7% and an F-measure of 64.9% for feature polarity classification and an accuracy of 89.8% and an F-measure of 89.7% for news polarity classification. The experimental results additionally show that the N_GRAM Around method provides the best average results.
Lead users play a vital role in next generation product development, as they help designers discover relevant product feature preferences months or even years before they are desired by the general customer base. Existing design methodologies proposed to extract lead user preferences are typically constrained by temporal, geographic, size, and heterogeneity limitations. To mitigate these challenges, the authors of this work propose a set of mathematical models that mine social media networks for lead users and the product features that they express relating to specific products. The authors hypothesize that: (i) lead users are discoverable from large scale social media networks and (ii) product feature preferences, mined from lead user social media data, represent product features that do not currently exist in product offerings but will be desired in future product launches. An automated approach to lead user product feature identification is proposed to identify latent features (product features unknown to the public) from social media data. These latent features then serve as the key to discovering innovative users from the ever increasing pool of social media users. The authors collect 2.1×109 social media messages in the United States during a period of 31 months (from March 2011 to September 2013) in order to determine whether lead user preferences are discoverable and relevant to next generation cell phone designs.
In this article, the influence of customers on product innovation is examined through a hypothesis testing study. Based on an analysis of relevant research, two hypotheses are developed: H1, customers are not the most significant factor positively affecting product innovation in new product development (NPD); and H2, customers have curved effects on product innovation in NPD. To test the hypotheses, an interactive multiple regression model and a curve fitting model are developed respectively. The simulation results showed that the most significant positive influence on product innovation results is made by external resources such as 'consultants, commercial lab or private R&D institutions'. Moreover, different amounts of input information provided by customers/clients/end-users have curved impacts on innovation results. It is suggested that firms should properly allocate their research focus on customers and other important factors. Moreover, it may be a promising way to improve product innovation through seeking support from external resources.
The idiosyncrasy of the Web has, in the last few years, been altered by Web 2.0 technologies and applications and the advent of the so-called Social Web. While users were merely information consumers in the traditional Web, they play a much more active role in the Social Web since they are now also data providers. The mass involved in the process of creating Web content has led many public and private organizations to focus their attention on analyzing this content in order to ascertain the general public’s opinions as regards a number of topics. Given the current Web size and growth rate, automated techniques are essential if practical and scalable solutions are to be obtained. Opinion mining is a highly active research field that comprises natural language processing, computational linguistics and text analysis techniques with the aim of extracting various kinds of added-value and informational elements from users’ opinions. However, current opinion mining approaches are hampered by a number of drawbacks such as the absence of semantic relations between concepts in feature search processes or the lack of advanced mathematical methods in sentiment analysis processes. In this paper we propose an innovative opinion mining methodology that takes advantage of new Semantic Web-guided solutions to enhance the results obtained with traditional natural language processing techniques and sentiment analysis processes. The main goals of the proposed methodology are: (1) to improve feature-based opinion mining by using ontologies at the feature selection stage, and (2) to provide a new vector analysis-based method for sentiment analysis. The methodology has been implemented and thoroughly tested in a real-world movie review-themed scenario, yielding very promising results when compared with other conventional approaches.
With the advent of the information and related emerging technologies, such as RFID, small size sensors and sensor networks or, more generally, product embedded information devices (PEID), a new generation of products called smart or intelligent products is available in the market.Although various definitions of intelligent products have been proposed, we introduce a new definition of the notion of Intelligent Product inspired by what happens in nature with us as human beings and the way we develop intelligence and knowledge. We see an intelligent product as a product system which contains sensing, memory, data processing, reasoning and communication capabilities at four intelligence levels. This future generations of Intelligent Products will need new Product Data Technologies allowing the seamless interoperability of systems and exchange of not only Static but of Dynamic Product Data as well. Actual standards for PDT cover only lowest intelligence of today’s products. In this context, we try to shape the actual state and a possible future of the Product Data Technologies from a Closed-Loop Product Lifecycle Management (C-L PLM) perspective.Our approach is founded in recent findings of the FP6 IP 507100 project PROMISE and follow-up research work. Standards of the STEP family, covering the product lifecycle to a certain extend (PLCS) as well as MIMOSA and ISO 15926 are discussed together with more recent technologies for the management of ID and sensor data such as EPCglobal, OGC-SWE and relevant PROMISE propositions for standards.Finally, the first efforts towards ontology based semantic standards for product lifecycle management and associated knowledge management and sharing are presented and discussed.
An important part of our information-gathering behavior has always been to find out what other people think. With the growing availability and popularity of opinion-rich resources such as online review sites and personal blogs, new opportunities and challenges arise as people now can, and do, actively use information technologies to seek out and understand the opinions of others. The sudden eruption of activity in the area, of opinion mining and sentiment analysis, which deals with the computational treatment of opinion, sentiment, and subjectivity in text, has thus occurred at least in part as a direct response to the surge of interest in new systems that deal directly with opinions as a first-class object. This survey covers techniques and approaches that promise to directly enable opinion-oriented information-seeking systems. Our focus is on methods that seek to address the new challenges raised by sentiment-aware applications, as compared to those that are already present in more traditional fact-based analysis. We include material on summarization of evaluative text and on broader issues regarding privacy, manipulation, and economic impact that the development of opinion-oriented information-access services gives rise to. To facilitate future work, a discussion of available resources, benchmark datasets, and evaluation campaigns is also provided.
this paper we shall use a narrower definition: the identification of instances of a particular class of events or relationships in a natural language text, and the extraction of the relevant arguments of the event or relationship. Information extraction therefore involves the creation of a structured representation (such as a data base) of selected information drawn from the text. The idea of reducing the information in a document to a tabular structure is not new. Its feasibility for sublanguage texts was suggested by Zellig Harris in the 1950's, and an early implementation for medical texts was done at New York University by Naomi Sager[20]. However, the specific notion of information extraction described here has received wide currency over the last decade through the series of Message Understanding Conferences [1, 2, 3, 4, 14]. We shall discuss these Conferences in more detail a bit later, and shall use simplified versions of extraction tasks from these Conferences as examples throughout this paper. Figure 1 shows a simplified example from one of the earlier MUC's, involving terrorist events (MUC-3) [1]. For each terrorist event, the system had to determine the type of attack (bombing, arson, etc.), the date, location, perpetrator (if stated), targets, and effects on targets. Other examples of extraction tasks are international joint ventures (where the arguments included the partners, the new venture, its product or service, etc.) and executive succession (indicating who was hired or fired by which company for which position). Information extraction is a more limited task than "full text understanding". In full text understanding, we aspire to represent in a explicit fashion all the information in a text. In contrast, in information extraction we delimit in advance...
Historically story understanding systems have depended on a great deal of handcrafted knowledge. Natural language understanding systems that use conceptual knowledge structures (Schank and Abelson 1977; Cullingford 1978; Wilensky 1978; Carbonell 1979; Lehnert 1981; Kolodner 1983) typically rely on enormous amounts of manual knowledge engineering. While much of the work on conceptual knowledge structures has been hailed as pioneering research in cognitive modeling and narrative understanding, from a practical perspective it has also been viewed with skepticism because of the underlying knowledge engineering bottleneck. The thought of building a large-scale conceptual natural language processing (NLP) system that can understand open-ended text is daunting even to the most ardent enthusiasts. So must we grit our collective teeth and assume that story understanding will be limited to prototype systems in the foreseeable future? Or will conceptual natural language processing ultimately depend on a massive, broad-scale manual knowledge engineering effort, such as CYC (Lenat, Prakash, and Shepherd 1986)?
Twitter remakes website, adds new features
- G Madway
Madway, G. (2010, September 14), Twitter remakes website, adds new features. Retrieved December 5, 2016,
from http://www.reuters.com/article/idUSN1411135520100915
Extraction patterns for information extraction tasks: a survey
- I Muslea
Muslea, I. (1999), Extraction patterns for information extraction tasks: a survey. In Proceedings of AAAI '99:
Workshop on Machine Learning for Information Extraction.
Extracting product features and opinions from reviews
- A.-M Popescu
- O Etzioni
Popescu, A.-M. and Etzioni, O. (2005), 'Extracting product features and opinions from reviews', Proceedings of
the conference on Human Language Technology and Empirical Methods in Natural Language Processing -HLT '05, the conference, Vancouver, British Columbia, Canada, 6/10/2005 -8/10/2005. Morristown, NJ,
USA: Association for Computational Linguistics, pp. 339-346. doi: 10.3115/1220575.1220618
Ontology based opinion mining for movie reviews
- L Zhao
- C Li
Zhao, L. and Li, C.(2009), Ontology based opinion mining for movie reviews. In 3rd International Conference
Knowledge, Science, Engineering and Management,2009.
- D M Blei
- A Y Ng
- M I Andjordan
Blei, D. M., Ng, A. Y. andJordan, M. I. (2003), Latent dirichlet allocation. the Journal of machine Learning
research, 3,993-1022.