scholarly journals Auto Defect Detection Using Customer Reviews for Product Recall Insurance Analysis

Author(s):  
Titus Hei Yeung Fong ◽  
Shahryar Sarkani ◽  
John Fossaceca

The challenge for Product Recall Insurance companies and their policyholders to manually explore their customer product’s defects from online customer reviews (OCR) delays product risk analysis and product recall recovery processes. In today's product life cycle, product recall events happen almost every day and there is no practical method to automatically transfer the massive amount of valuable online customer reviews, such as defect information, performance issue, and serviceability feedback, to the Product Recall Insurance team as well as their policyholders’ engineers to analyze the product risk and evaluate their premium. This lack of early risk analysis and defect detection mechanism often increases the risks of a product recall and cost of claims for both the insurers and policyholder, potentially causing billions of dollars in economic loss, liability resulting from the bodily injury, and loss of company credibility. This research explores two different kinds of Recurrent Neural Network (RNN) models and one Latent Dirichlet Allocation (LDA) topic model to extract product defect information from OCRs. This research also proposes a novel approach, combined with RNN and LDA models, to provide the insurers and the policyholders with an early view of product defects. The proposed approach first employs the RNN models for sentiment analysis on customer reviews to identify negative reviews and reviews that mention product defects, then applies the LDA model to retrieve a summary of key defect insight words from these reviews. Results of this research show that both the insurers and the policyholders can discover early signs of potential defects and opportunities for improvement when using this novel approach on eight of the bestselling Amazon home furnishing products. This combined approach can locate the keywords of these products’ defects and issues that customers mentioned the most in their OCRs, which allows the insurers and the policyholders to take required mitigation actions earlier, proactively stop the diffusion of the detective products, and hence lower the cost of claim and premium.

2020 ◽  
pp. 1-10
Author(s):  
Junegak Joung ◽  
Harrison M. Kim

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.


2020 ◽  
Vol 12 (7) ◽  
pp. 2843 ◽  
Author(s):  
Eunhye (Olivia) Park ◽  
Bongsug (Kevin) Chae ◽  
Junehee Kwon ◽  
Woo-Hyuk Kim

Although green practice is increasingly adopted in the restaurant industry, there is still little research in terms of investigating the impacts of green practice on customer satisfaction. This study utilized user-generated content by green restaurant customers to identify various aspects of green restaurants, including perceived green restaurant practices. Our data are based on U.S. green-certified restaurants available on Yelp. Structural topic modeling was used to discover latent restaurant attributes from user-generated content. With a longitudinal approach, the changes in customers’ interest in green practices were estimated. Finally, the common restaurant attributes and green attributes were used to predict customer satisfaction. This study will contribute to marketing strategies for the restaurant industry.


Author(s):  
Muhammad Bilal ◽  
Mohsen Marjani ◽  
Ibrahim Abaker Targio Hashem ◽  
Nadia Malik ◽  
Muhammad Ikram Ullah Lali ◽  
...  

Author(s):  
Xi Liu ◽  
Yongfeng Yin ◽  
Haifeng Li ◽  
Jiabin Chen ◽  
Chang Liu ◽  
...  

AbstractExisting software intelligent defect classification approaches do not consider radar characters and prior statistics information. Thus, when applying these appaoraches into radar software testing and validation, the precision rate and recall rate of defect classification are poor and have effect on the reuse effectiveness of software defects. To solve this problem, a new intelligent defect classification approach based on the latent Dirichlet allocation (LDA) topic model is proposed for radar software in this paper. The proposed approach includes the defect text segmentation algorithm based on the dictionary of radar domain, the modified LDA model combining radar software requirement, and the top acquisition and classification approach of radar software defect based on the modified LDA model. The proposed approach is applied on the typical radar software defects to validate the effectiveness and applicability. The application results illustrate that the prediction precison rate and recall rate of the poposed approach are improved up to 15 ~ 20% compared with the other defect classification approaches. Thus, the proposed approach can be applied in the segmentation and classification of radar software defects effectively to improve the identifying adequacy of the defects in radar software.


2019 ◽  
Vol 13 (2) ◽  
pp. 249-275
Author(s):  
Jake David Hoskins ◽  
Ryan Leick

Purpose This study aims to investigate a sharing economy context, where vacation rental units that are owned and operated by individuals throughout the world are rented out through a common website: vrbo.com. It is posited that gross domestic product (GDP) per capita, a common indicator of the level of economic development of a nation, will impact the likelihood that prospective travelers will choose to book accommodations in the sharing economy channel (vs traditional hotels). The role of online customer reviews in this process is investigated as well, building upon a significant body of extant research which shows their level of customer decision influence. Design/methodology/approach An empirical analysis is conducted using data from the website Vacation Rentals By Owner on 1,940 rental listings across 97 countries. Findings GDP per capita serves as risk deterrent to prospective travelers, making the sharing economy an acceptable alternative to traditional hotels for the average traveler. It is also found that the total number of online customer reviews (OCR volume) is a signal of popularity to prospective travelers, while the average star rating of those online customer reviews (OCR valence) is instead a signal of accommodation quality. Originality/value This study adds to a growing agenda of research investigating the effect of online customer reviews on consumer decisions, with a particularly focus on the burgeoning sharing economy. The findings help to explain when the sharing economy may serve as a stronger disruptive threat to incumbent offerings. It also provides the following key insights for managers: sharing economy rental units in developed nations are more successful in driving booking activity, managers should look to promote volume of online customer reviews and positive online customer reviews are particularly influential for sharing economy rental booking rates in less developed nations.


Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 415
Author(s):  
Jinli Wang ◽  
Yong Fan ◽  
Hui Zhang ◽  
Libo Feng

Tracking scientific and technological (S&T) research hotspots can help scholars to grasp the status of current research and develop regular patterns in the field over time. It contributes to the generation of new ideas and plays an important role in promoting the writing of scientific research projects and scientific papers. Patents are important S&T resources, which can reflect the development status of the field. In this paper, we use topic modeling, topic intensity, and evolutionary computing models to discover research hotspots and development trends in the field of blockchain patents. First, we propose a time-based dynamic latent Dirichlet allocation (TDLDA) modeling method based on a probabilistic graph model and knowledge representation learning for patent text mining. Second, we present a computational model, topic intensity (TI), that expresses the topic strength and evolution. Finally, the point-wise mutual information (PMI) value is used to evaluate topic quality. We obtain 20 hot topics through TDLDA experiments and rank them according to the strength calculation model. The topic evolution model is used to analyze the topic evolution trend from the perspectives of rising, falling, and stable. From the experiments we found that 8 topics showed an upward trend, 6 topics showed a downward trend, and 6 topics became stable or fluctuated. Compared with the baseline method, TDLDA can have the best effect when K is 40 or less. TDLDA is an effective topic model that can extract hot topics and evolution trends of blockchain patent texts, which helps researchers to more accurately grasp the research direction and improves the quality of project application and paper writing in the blockchain technology domain.


2021 ◽  
pp. 002224372110444
Author(s):  
Zijun (June) Shi ◽  
Xiao Liu ◽  
Kannan Srinivasan

Consumers' choices about health products are heavily influenced by public information, such as news articles, research articles, online customer reviews, online product discussion, and TV shows. Dr. Oz, a celebrity doctor, often makes medical recommendations with limited or marginal scientific evidence. Although reputable news agencies have traditionally acted as gatekeepers of reliable information, they face the intense pressure of “the eyeball game.” Customer reviews, despite their authenticity, may come from deceived consumers. Therefore, it remains unclear whether public information sources can correct the misleading health information. In the context of over-the-counter weight loss products, the authors carefully analyze the cascading of information post endorsement. The analysis of extensive textual content with deep-learning methods reveals that legitimate news outlets respond to Dr. Oz's endorsement by generating more news articles about the ingredient; on average, articles after the endorsement contain a higher sentiment, so news agencies seem to amplify rather than rectify the misleading endorsement. The finding highlights a serious concern: the risk of hype news diffusion. Research articles react too slowly to mitigate the problem, and online customer reviews and product discussions provide only marginal corrections. The findings underscore the importance of oversight to mitigate the risk of cascading hype news.


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