Automated Mapping of Product Features Mined From Online Customer Reviews to Engineering Product Characteristics

2016 ◽  
Author(s):  
Sung woo Kang ◽  
Conrad S. Tucker
Keyword(s):  
New Product Development ◽  
Large Scale ◽  
Engineering Product ◽  
Automated Mapping ◽  
Customer Reviews ◽  
Pagerank Algorithm ◽  
Product Features ◽  
Online Customer Reviews ◽  
Scale Design

Until now, translating product features expressed in the market into quantifiable engineering metrics has primarily been a manual process. This manual process establishes product features from large-scale customer feedback using a product’s components from large-scale design specifications. This process exacerbates the complexity and sheer amount of information that designers must handle during the early stages of new product development. The methodology proposed in this paper automatically identifies product features by mapping terms that describe product features from technical descriptions and customer reviews. In order to discover terms related to the features expressed in the market, the authors of this work employ WordNet and the PageRank algorithm, which search for semantically similar terms in products’ technical descriptions. A case study demonstrates the methodology’s viability for matching product features that are extracted from online customer reviews to the technical descriptions that address them.

The Boeing 787 Dreamliner--A case study in large-scale design integration

2008 ◽  
Author(s):  
D. L. McMullin ◽  
A. R. Jacobsen ◽  
D. C. Carvan ◽  
R. J. Gardner ◽  
J. A. Goegan ◽  
...  
Keyword(s):  
Large Scale ◽  
Design Integration ◽  
Scale Design

The Boeing 787 Dreamliner – a Case Study in Large-Scale Design Integration

2008 ◽  
Vol 52 (20) ◽  
pp. 1670-1671 ◽  
Author(s):  
D. L. McMullin ◽  
A. R. Jacobsen ◽  
D. C. Carvan ◽  
R. J. Gardner ◽  
J. A. Goegan ◽  
...  
Keyword(s):  
Large Scale ◽  
Design Integration ◽  
Scale Design

scholarly journals The Effect of Ease of Shopping, Online Customer Reviews and Maslahah Perceptions on Gen Z Online Shopping Decisions (Case Study Of UINSI and UMKT Samarinda Students)

2021 ◽  
Vol 1 ◽  
pp. 179-193
Author(s):  
Rogaina Rogaina ◽  
Tikawati Tikawati
Keyword(s):  
Regression Analysis ◽  
Data Analysis ◽  
Multiple Regression Analysis ◽  
Multiple Regression ◽  
Online Shopping ◽  
Field Research ◽  
Customer Reviews ◽  
Online Customer Reviews ◽  
Independent Variable

This study aims to examine and analyse the effect of ease of shopping, online customer reviews and perceptions of maslahah on online shopping decisions among UINSI and UMKT Samarinda students. The type of research used is field research. The sample in this study was 198 people who had shopped online. The method of collecting data is a questionnaire is distributed online using Google form. Data analysis used multiple regression analysis. The results showed that of the three variables of ease of shopping, online customer review and perception of maslahah simultaneously affect online shopping decisions. From the calculation of SPSS 23. For the Ftest, it is known that Fcount = 187.146 > Ftable 2.65 with a significance of 0.000 < 0.5. Partially, it is known that the ease of shopping, online customer reviews and the perception of maslahah have a significant effect of online shopping decisions. In addition to the Ftest and t-test, the R2 test is known to have an R square value of 0.743 which means the magnitude of the independent variable 74.3%.

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.

scholarly journals Research on the Evaluation of Customer requirement Importance of Smart Products Based on Online Comments and Improved Quantitative Kano Model

CONVERTER ◽  
2021 ◽  
pp. 382-392
Author(s):  
Hang Liu, Zan Ren, Yingjie Li
Keyword(s):  
Customer Requirement ◽  
Kano Model ◽  
Customer Reviews ◽  
Technological Differences ◽  
Online Customer Reviews ◽  
Smart Products ◽  
Online Comments ◽  
Product Manufacturing ◽  
Product Homogeneity

With the development and popularization of smart products, the technological differences of products are decreasing, and the phenomenon of product homogeneity is becoming more and more obvious. It is necessary for the smart product manufacturing firms have the capability to analyze customer requirement deeply and adapt to the dynamically changing market quickly. Therefore, the traditional technology-oriented product development model is no longer suitable for manufacturers to obtain a competitive advantage. Based on this, this paper proposed a method to evaluate the importance of customer demands based on online comments and quantitative Kano model. First, the Python crawler tool is used to obtain online customer reviews of relevant products and the word segmentation processing is performed to obtain the product features and frequency that customers are mainly concerned about, and then the initial importance of demand can be calculated. Furthermore, use the quantitative Kano model to determine the customer satisfaction and revise the initial importance of the requirements to obtain a more reasonable ranking of the importance of user needs. Finally, a case study is carried out with the smart bracelet as an example to verify the effectiveness and feasibility of the model proposed in this paper.

Application of social media analytics: a case of analyzing online hotel reviews

2017 ◽  
Vol 41 (7) ◽  
pp. 921-935 ◽  
Author(s):  
Wu He ◽  
Xin Tian ◽  
Ran Tao ◽  
Weidong Zhang ◽  
Gongjun Yan ◽  
...  
Keyword(s):  
Social Media ◽  
Text Mining ◽  
Social Media Analytics ◽  
Social Media Data ◽  
Content Type ◽  
Customer Reviews ◽  
Analysis Techniques ◽  
Online Customer Reviews ◽  
Media Data

Purpose Online customer reviews could shed light into their experience, opinions, feelings, and concerns. To gain valuable knowledge about customers, it becomes increasingly important for businesses to collect, monitor, analyze, summarize, and visualize online customer reviews posted on social media platforms such as online forums. However, analyzing social media data is challenging due to the vast increase of social media data. The purpose of this paper is to present an approach of using natural language preprocessing, text mining and sentiment analysis techniques to analyze online customer reviews related to various hotels through a case study. Design/methodology/approach This paper presents a tested approach of using natural language preprocessing, text mining, and sentiment analysis techniques to analyze online textual content. The value of the proposed approach was demonstrated through a case study using online hotel reviews. Findings The study found that the overall review star rating correlates pretty well with the sentiment scores for both the title and the full content of the online customer review. The case study also revealed that both extremely satisfied and extremely dissatisfied hotel customers share a common interest in the five categories: food, location, rooms, service, and staff. Originality/value This study analyzed the online reviews from English-speaking hotel customers in China to understand their preferred hotel attributes, main concerns or demands. This study also provides a feasible approach and a case study as an example to help enterprises more effectively apply social media analytics in practice.

Mining opinionated product features using WordNet lexicographer files

2016 ◽  
Vol 43 (6) ◽  
pp. 769-785 ◽  
Author(s):  
Saif A. Ahmad Alrababah ◽  
Keng Hoon Gan ◽  
Tien-Ping Tan
Keyword(s):  
Assessment Tool ◽  
Empirical Evaluation ◽  
Online Reviews ◽  
Product Reviews ◽  
Specific Domain ◽  
Customer Reviews ◽  
Product Features ◽  
Online Customer Reviews ◽  
Customer Perspective ◽  
Massive Number

Online customer reviews are an important assessment tool for businesses as they contain feedback that is valuable from the customer perspective. These reviews provide a significant basis on which potential customers can select the product that best meets their preferences. In online reviews, customers describe positive or negative experiences with a product or service or any part of it (i.e. features). Consumers frequently experience difficulty finding the desired product for comparison because of the massive number of online reviews. The automatic extraction of important product features is necessary to support customers in search of relevant product features. These features are the criteria that make it possible for customers to characterise different types of products. This article proposes a domain independent approach for identifying explicit opinionated features and attributes that are strongly related to a specific domain product using lexicographer files in WordNet. In our approach, N_gram analysis and the SentiStrength opinion lexicon have been employed to support the extraction of opinionated features. The empirical evaluation of the proposed system using online reviews of two popular datasets of supervised and unsupervised systems showed that our approach achieved competitive results for feature extraction from product reviews.

Mitigating Online Product Rating Biases Through the Discovery of Optimistic, Pessimistic, and Realistic Reviewers

2017 ◽  
Vol 139 (11) ◽  
Author(s):  
Sunghoon Lim ◽  
Conrad S. Tucker
Keyword(s):  
Large Scale ◽  
Training Data ◽  
Purchasing Decisions ◽  
Analysis Model ◽  
Rating Data ◽  
Star Rating ◽  
Customer Reviews ◽  
Online Customer Reviews ◽  
Product Ratings ◽  
Product Rating

The authors of this work present a model that reduces product rating biases that are a result of varying degrees of customers' optimism/pessimism. Recently, large-scale customer reviews and numerical product ratings have served as substantial criteria for new customers who make their purchasing decisions through electronic word-of-mouth. However, due to differences among reviewers' rating criteria, customer ratings are often biased. For example, a three-star rating can be considered low for an optimistic reviewer. On the other hand, the same three-star rating can be considered high for a pessimistic reviewer. Many existing studies of online customer reviews overlook the significance of reviewers' rating histories and tendencies. Considering reviewers' rating histories and tendencies is significant for identifying unbiased customer ratings and true product quality, because each reviewer has different criteria for buying and rating products. The proposed customer rating analysis model adjusts product ratings in order to provide customers with more objective and accurate feedback. The authors propose an unsupervised model aimed at mitigating customer ratings based on rating histories and tendencies, instead of human-labeled training data. A case study involving real-world customer rating data from an electronic commerce company is used to validate the method.

Predicting Future Importance of Product Features Based on Online Customer Reviews

2017 ◽  
Vol 139 (11) ◽  
Author(s):  
Huimin Jiang ◽  
C. K. Kwong ◽  
K. L. Yung
Keyword(s):  
Time Series ◽  
Opinion Mining ◽  
Fuzzy Inference ◽  
Online Reviews ◽  
Fuzzy Time Series ◽  
Series Method ◽  
Customer Reviews ◽  
Product Features ◽  
Online Customer Reviews ◽  
Time Series Method

Previous studies conducted customer surveys based on questionnaires and interviews, and the survey data were then utilized to analyze product features. In recent years, online customer reviews on products became extremely popular, which contain rich information on customer opinions and expectations. However, previous studies failed to properly address the determination of the importance of product features and prediction of their future importance based on online reviews. Accordingly, a methodology for predicting future importance weights of product features based on online customer reviews is proposed in this paper which mainly involves opinion mining, a fuzzy inference method, and a fuzzy time series method. Opinion mining is adopted to analyze the online reviews and extract product features. A fuzzy inference method is used to determine the importance weights of product features using both frequencies and sentiment scores obtained from opinion mining. A fuzzy time series method is adopted to predict the future importance of product features. A case study on electric irons was conducted to illustrate the proposed methodology. To evaluate the effectiveness of the fuzzy time series method in predicting the future importance, the results obtained by the fuzzy time series method are compared with those obtained by the three common forecasting methods. The results of the comparison show that the prediction results based on fuzzy time series method are better than those based on exponential smoothing, simple moving average, and fuzzy moving average methods.

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