scholarly journals Automatic Identification of Product Usage Contexts from Online Customer Reviews

2019 ◽  
Vol 1 (1) ◽  
pp. 2507-2516
Author(s):  
Dedy Suryadi ◽  
Harrison Kim
Keyword(s):  
Product Design ◽  
Automatic Identification ◽  
Computing Power ◽  
Product Usage ◽  
Customer Reviews ◽  
Customer Preferences ◽  
Context Factors ◽  
Online Customer Reviews ◽  
Product Description ◽  
Usage Context

AbstractThere are three product design contexts that may significantly affect the design of a product and customer preferences towards product attributes, i.e. customer context, market context, and usage context factors. The conventional methods to gather product usage contexts may be costly and time consuming to conduct. As an alternative, this paper aims to automatically identify product usage contexts from publicly available online customer reviews. The proposed methodology consists of Preprocessing, Word Embedding, and Usage Context Clustering stages. The methodology is applied to identify usage contexts from laptop customer reviews, which results in 16 clusters of usage contexts. Furthermore, analyzing the review sentences explains the separation of “playing games” –which is more related to casual gaming, and “gaming rig” –which implies high computing power requirements. Finally, comparing customer review with manufacturer's product description may reveal a discrepancy to be investigated further by product designer, e.g. a customer suggests a laptop for basic use, although the manufacturer's description describes it for heavy use.

A Data-Driven Approach to Product Usage Context Identification From Online Customer Reviews

2019 ◽  
Vol 141 (12) ◽  
Author(s):  
Dedy Suryadi ◽  
Harrison M. Kim
Keyword(s):  
Sentiment Analysis ◽  
Language Processing ◽  
Consumer Satisfaction ◽  
Data Driven ◽  
Data Sets ◽  
Product Usage ◽  
Customer Reviews ◽  
Online Customer Reviews ◽  
Data Driven Approach ◽  
Usage Context

Abstract This paper proposes a data-driven methodology to automatically identify product usage contexts from online customer reviews. Product usage context is one of the factors that affect product design, consumer behavior, and consumer satisfaction. The previous works identify the usage contexts using the survey-based method or subjectively determine them. The proposed methodology, on the other hand, uses machine learning and Natural Language Processing tools to identify and cluster usage contexts from a large volume of customer reviews. Furthermore, aspect sentiment analysis is applied to capture the sentiment toward a particular usage context in a sentence. The methodology is implemented to two data sets of products, i.e., laptop and tablet. The result shows that the methodology is able to capture relevant product usage contexts and cluster bigrams that refer to similar usage context. The aspect sentiment analysis enables the observation of a product’s position with respect to its competitors for a particular usage context. For a product designer, the observation may indicate a requirement to improve the product. It may also indicate a possible market opportunity in a usage context in which most of the current products are perceived negatively by customers. Finally, it is shown that overall rating might not be a strong indicator for representing customer sentiment toward a particular usage context, due to the moderate linear correlation for most of the usage contexts in the case study.

Automated Keyword Filtering in LDA for Identifying Product Attributes from Online Reviews

2020 ◽  
pp. 1-10
Author(s):  
Junegak Joung ◽  
Harrison M. Kim
Keyword(s):  
Product Design ◽  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
Online Reviews ◽  
Previous Method ◽  
Product Attributes ◽  
Customer Reviews ◽  
Online Customer Reviews ◽  
Dirichlet Allocation

Abstract Identifying product attributes from the perspective of a customer is essential to measure the satisfaction, importance, and Kano category of each product attribute for product design. This paper proposes automated keyword filtering to identify product attributes from online customer reviews based on latent Dirichlet allocation. The preprocessing for latent Dirichlet allocation is important because it affects the results of topic modeling; however, previous research performed latent Dirichlet allocation either without removing noise keywords or by manually eliminating them. The proposed method improves the preprocessing for latent Dirichlet allocation by conducting automated filtering to remove the noise keywords that are not related to the product. A case study of Android smartphones is performed to validate the proposed method. The performance of the latent Dirichlet allocation by the proposed method is compared to that of a previous method, and according to the latent Dirichlet allocation results, the former exhibits a higher performance than the latter.

Product “In-Use” Context Identification Using Feature Learning Methods

2016 ◽  
Author(s):  
Dipanjan D. Ghosh ◽  
Andrew Olewnik ◽  
Kemper Lewis
Keyword(s):  
Machine Learning ◽  
Product Design ◽  
Learning Algorithms ◽  
Feature Learning ◽  
Machine Learning Algorithms ◽  
Learning Methods ◽  
Product Usage ◽  
Feature Based ◽  
Product Interaction ◽  
Usage Context

Usage context is considered a critical driving factor for customers’ product choices. In addition, the physical use of a product (i.e., user-product interaction) dictates a number of customer perceptions (e.g. level of comfort, ease-of-use or users’ physical fatigue). In the emerging Internet-of-Things (IoT), this work hypothesizes that it is possible to understand product usage while it is ‘in-use’ by capturing the user-product interaction data. Mining the data and understanding the comfort of the user adds a new dimension to the product design field. There has been tremendous progress in the field of data analytics, but the application in product design is still nascent. In this work, application of ‘feature learning’ methods for the identification of product usage context is demonstrated, where usage context is limited to the activity of the user. Two feature learning methods are applied for a walking activity classification using smartphone accelerometer data. Results are compared with feature-based machine learning algorithms (neural networks and support vector machines), and demonstrate the benefits of using the ‘feature learning’ methods over the feature based machine-learning algorithms.

Effects of Product Usage Context on Consumer Product Preferences

2005 ◽  
Author(s):  
Matthew G. Green ◽  
Junjay Tan ◽  
Julie S. Linsey ◽  
Carolyn C. Seepersad ◽  
Kristin L. Wood
Keyword(s):  
Product Design ◽  
Contextual Information ◽  
Test Methods ◽  
Valuable Insight ◽  
Product Usage ◽  
Functional Families ◽  
Product Preferences ◽  
Future Work ◽  
Usage Context

We present a framework for understanding product usage context and its impact upon customer needs and product preferences. We conduct customer interviews with two sets of representative products from the functional families of “mobile lighting” and “food boiling” products. Customer interviews lead to identification and characterization of distinct product usage contexts. Interactive surveys measuring customer product choice support the hypothesis that customer product preferences differ for each usage context identified. Further analysis shows that attributes of these chosen products are related to factors of the usage context (e.g. mass is related to transportation mode). These results demonstrate that valuable insight for product design is available through an understanding of usage context, and future work will refine and test methods to formally bring contextual information to bear on product design. These capabilities will be especially important for contexts in which needs assessment has traditionally been difficult, such as with latent needs and frontier design environments.

scholarly journals Definition of customer requirements in big data using word vectors and affinity propagation clustering

2021 ◽  
pp. 095440892110017
Author(s):  
Yanlin Shi ◽  
Qingjin Peng
Keyword(s):  
Big Data ◽  
Product Design ◽  
Affinity Propagation ◽  
Product Reviews ◽  
Customer Requirements ◽  
Parts Of Speech ◽  
Customer Reviews ◽  
Affinity Propagation Clustering ◽  
Online Customer Reviews ◽  
Definition Of

Customer requirements (CRs) have a significant impact on product design. The existing methods of defining CRs, such as customer surveys and expert evaluations, are time-consuming, inaccurate and subjective. This paper proposes an automatic CRs definition method based on online customer product reviews using the big data analysis. Word vectors are defined using a continuous bag of words (CBOW) model. Online customer reviews are searched by a crawling method and filtered by the parts of speech and frequency of words. Filtered words are then clustered into groups by an affinity propagation (AP) clustering method based on trained word vectors. Exemplars in each clustering group are finally used to define CRs. The proposed method is verified by case studies of defining CRs for product design. Results show that the proposed method has better performance to determine CRs compared to existing CRs definition methods.

scholarly journals Exploiting online customer reviews for product design

2020 ◽  
Vol 909 ◽  
pp. 012080
Author(s):  
Yanti Pasmawati ◽  
Alva Edy Tontowi ◽  
Budi Hartono ◽  
Titis Wijayanto
Keyword(s):  
Product Design ◽  
Customer Reviews ◽  
Online Customer Reviews

scholarly journals Integrating Hedonic Quality for User Experience Modelling

2021 ◽  
Author(s):  
Yanzhang Tong ◽  
Yan Liang ◽  
Ying Liu ◽  
Yulia Hicks ◽  
Irena Spasic
Keyword(s):  
Product Design ◽  
User Experience ◽  
User Interaction ◽  
Word List ◽  
Quality Factors ◽  
Customer Reviews ◽  
Online Customer Reviews ◽  
Design Activities ◽  
Customer Review ◽  
Hedonic Quality

Abstract Research on user experience (UX) has attracted much attention from designers. Additionally, hedonic quality can help designers understand user interaction (such as attractive, original and innovative) when they experience a product. Realising the user’s interaction state is a significant step for designers to optimise product design and service. Previous UX modelling lacks exploration in user interaction state. Also, the lack of user interaction state factor will reduce the accuracy of the UX modelling. In this paper, we explore the interaction value of online customer review and introduce a new approach to integrating hedonic quality for UX modelling. Firstly, extracting word list from online customer review; Secondly, hedonic quality words are extracted from the word list and added as a hedonic quality part to UX modelling; Thirdly, we compared the analysis result with our previous study for the conclusion. This research combines hedonic quality with UX modelling to enrich modelling in the field of UX for the first time. The proposed data collection method is superior to the traditional collection methods in hedonic quality studies. Extracting hedonic quality factors from online customer reviews can in-depth provide reflections for designers to improve their product design. Furthermore, it also explored the valuable relationship between UX and online customer reviews to provide proactive thinking in user strategy and design activities.

scholarly journals Application of Feature-Learning Methods Toward Product Usage Context Identification and Comfort Prediction

2017 ◽  
Vol 18 (1) ◽  
Author(s):  
Dipanjan Ghosh ◽  
Andrew Olewnik ◽  
Kemper Lewis
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Product Design ◽  
Feature Learning ◽  
Machine Learning Algorithms ◽  
Learning Methods ◽  
Product Usage ◽  
Feature Based ◽  
Product Interaction ◽  
Usage Context

Usage context is considered a critical driving factor for customers' product choices. In addition, physical use of a product (i.e., user-product interaction) dictates a number of customer perceptions (e.g., level of comfort). In the emerging internet of things (IoT), this work hypothesizes that it is possible to understand product usage and level of comfort while it is “in-use” by capturing the user-product interaction data. Mining this data to understand both the usage context and the comfort of the user adds new capabilities to product design. There has been tremendous progress in the field of data analytics, but the application in product design is still nascent. In this work, application of feature-learning methods for the identification of product usage context and level of comfort is demonstrated, where usage context is limited to the activity of the user. A novel generic architecture using foundations in convolutional neural network (CNN) is developed and applied to a walking activity classification using smartphone accelerometer data. Results are compared with feature-based machine learning algorithms (neural network and support vector machines (SVM)) and demonstrate the benefits of using the feature-learning methods over the feature-based machine-learning algorithms. To demonstrate the generic nature of the architecture, an application toward comfort level prediction is presented using force sensor data from a sensor-integrated shoe.

User Perception on Mixed Attribute of Intelligent Product According to Product Usage Context

2017 ◽  
Vol 4 (1) ◽  
pp. 13-20
Author(s):  
Dahyun Kang ◽  
◽  
Min-Gyu Kim ◽  
Sonya S. Kwak ◽  
◽  
...  
Keyword(s):  
User Perception ◽  
Product Usage ◽  
Intelligent Product ◽  
Usage Context
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