Review
Engineering, Industrial
Junegak Joung, Harrison M. Kim
Summary: The paper introduces a neural network-based approach to classify product features into Kano categories using online reviews, demonstrating higher reliability and efficiency in Kano analysis.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2022)
Article
Computer Science, Artificial Intelligence
Shunxiang Zhang, Hanqing Xu, Guangli Zhu, Xiang Chen, KuanChing Li
Summary: New sentiment words in product reviews are valuable resources closely related to users. A data processing method based on sequence labeling and syntactic analysis is proposed to extract new sentiment words from product reviews, which significantly improves the accuracy and recall rate of sentiment analysis, as demonstrated by experimental results.
Article
Business
Fan Zou, Yupeng Li, Jiahuan Huang
Summary: This study proposes a Customer Reviews Evolution Model (CREM) to analyze the dynamic evolution process of group customer reviews by simulating the dynamic interaction among customers. The model takes into account the negativity bias and the helpfulness of reviews to better represent customers' sensitivity to reviews and information acquisition ability. Case study results support the feasibility and effectiveness of the proposed model in supporting attribute evaluation in product redesign.
ELECTRONIC COMMERCE RESEARCH
(2022)
Review
Computer Science, Information Systems
Fangmin Cheng, Suihuai Yu, Jianjie Chu, Jiashuang Fan, Yukun Hu
Summary: This paper proposes a customer satisfaction-oriented product configuration approach based on online product reviews. By using quantitative Kano model and single objective programming model, it can effectively improve customer satisfaction of product configuration schemes and save labor, time, and cost.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Review
Computer Science, Information Systems
Sharon Hirsch, Slava Novgorodov, Ido Guy, Alexander Nus
Summary: Product reviews are crucial for e-commerce platforms, as users heavily rely on them before making purchases. However, extracting useful information from a large number of reviews can be challenging. To address this, some websites have introduced the option of posting tips, which provide practical insights about products. In this study, we propose a method to extract tips from product reviews in popular e-commerce domains. We use a supervised approach and evaluate different methods, demonstrating high performance in certain domains. We also discuss the practical applications of our approach in real-world scenarios, benefiting both buyers and sellers.
ACM TRANSACTIONS ON INTERNET TECHNOLOGY
(2023)
Article
Computer Science, Artificial Intelligence
L. D. C. S. Subhashini, Yuefeng Li, Jinglan Zhang, Ajantha S. Atukorale, Yutong Wu
Summary: With the increasing number of customer reviews on the Web, there is a growing demand for effective methods to retrieve valuable information from reviews. Researchers have proposed many automatic mining and classification methods, but choosing a trusted method remains a challenge for companies. This article surveys recent opinion mining literature, focusing on text feature extraction, knowledge representation, and classification methods.
ARTIFICIAL INTELLIGENCE REVIEW
(2021)
Article
Chemistry, Multidisciplinary
Przemyslaw Kucharski, Krzysztof Slot
Summary: This paper proposes a novel approach to detect both known and unknown polymorphic patterns in symbol sequences. By representing rules as regular expressions and using filter cascades of neural convolutional layers, a convolutional autoencoder is utilized for pattern detection. The method successfully detects the presence of unknown polymorphic patterns.
APPLIED SCIENCES-BASEL
(2023)
Article
Chemistry, Multidisciplinary
Fahad M. Alotaibi
Summary: Machine learning frameworks have improved sales and product quality for major manufacturers by categorizing customer reviews on online products. Manual scrutiny of extensive customer reviews is imprecise and time-consuming. Current research techniques neglect audio and image components, resulting in less productive outcomes. AI-based frameworks that consider social media and online buyer reviews are essential for accurate recommendations.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Cybernetics
Ahmed Ibrahim, Sarah Clinch, Simon Harper
Summary: This study proposes a general framework for understanding human behavior by processing notifications. The researchers successfully establish a connection between buying behavior and notifications through a specific use case.
BEHAVIOUR & INFORMATION TECHNOLOGY
(2022)
Review
Management
Linyi Li, Shyam Gopinath, Stephen J. Carson
Summary: The study found that positive reviews of the previous generation product have a positive impact on the sales of the current generation product, while positive reviews of the current generation product have a negative impact on the sales of the previous generation product. The positive impact of previous generation reviews becomes stronger with higher uncertainty in current generation reviews and higher positivity in current generation reviews.
MANAGEMENT SCIENCE
(2022)
Article
Information Science & Library Science
Aditya Nugroho, Wei-Tsong Wang
Summary: This research aims to examine the factors influencing customers' product return intentions and proposes that YouTube product reviews can mitigate this desire. The study used a quantitative approach with data from 302 Indonesian young adults, analyzing the structural model using SmartPLS 3.2 software. The results indicate that YouTube product reviews, product fit uncertainty, and customer satisfaction are key determinants of product return intention, with the credibility of YouTube reviews having a major impact on familiarity, satisfaction, and likelihood of returning goods.
INFORMATION TECHNOLOGY & PEOPLE
(2023)
Article
Operations Research & Management Science
Cui Zhao, Xiaoshuai Peng, Zhendong Li
Summary: We study quality and pricing decisions of two competing firms in an e-marketplace with online customer reviews. Our results show that online reviews tend to encourage firms to increase quality and charge low prices in the early stage, while decreasing quality and raising prices in the later stage. Firms should choose optimal product strategies based on the impact of customers' private assessment of product quality and customer uncertainty about the perceived degree of product fit. The dual-element dynamic strategy is likely to outperform other strategies financially.
ANNALS OF OPERATIONS RESEARCH
(2023)
Article
Business
Haksin Chan, Morgan X. Yang, Kevin J. Zeng
Summary: This research focuses on the strategic design of ratings and reviews systems on multi-sided platforms to ensure a steady flow of buyer-generated product knowledge. It presents a theoretical model and identifies exemplary practices through exploratory observations, contributing to theory and practice in the field of multi-sided platforms.
JOURNAL OF BUSINESS RESEARCH
(2022)
Article
Computer Science, Information Systems
Mohsen Ramezani, Fardin Akhlaghian Tab, Alireza Abdollahpouri, Mahmud Abdulla Mohammad
Summary: A new collaborative filtering method is proposed in this paper, which overcomes sparsity challenge by finding similar users directly and indirectly. The method selects users through extracting dominant opinion patterns and outperforms previously introduced methods, especially on sparse data.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Interdisciplinary Applications
Mehdi Rajabi Asadabadi, Morteza Saberi, Nima Salehi Sadghiani, Ofer Zwikael, Elizabeth Chang
Summary: This paper proposes an automated approach to quality management and product improvement using online product reviews. By performing text mining, it effectively captures the voice of the customer and utilizes the extracted information to guide the product improvement process. This approach enhances quality management processes in organizations and advances customer-oriented product improvement.
JOURNAL OF ENTERPRISE INFORMATION MANAGEMENT
(2023)
