Online Reviews Sentiment Analysis and Product Feature Improvement with Deep Learning

Cao, Jihua; Li, Jie; Yin, Miao; Wang, Yunfeng

doi:10.1145/3522575

Cited by 14 publications

(4 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Traditional product feature analysis research focuses solely on customer requirements for already developed product features. This approach is useful for improving existing product features but is not suitable for developing new products [62][63][64]. In the context of new product development, analyzing online reviews from a customer observation perspective as conducted in this study is more appropriate and beneficial for developing new product concepts and assessing the value of applied technologies [65,66].…”

Section: Uniqueness and Contributionmentioning

confidence: 99%

Online Review Analysis from a Customer Behavior Observation Perspective for Product Development

Lee,

Chung,

Park

2024

Sustainability

View full text Add to dashboard Cite

Observing customers is one of the methods to uncover their needs. By closely observing how customers use products, we can indirectly experience their interactions and gain a deep understanding of their feelings and preferences. Through this process, companies can design new products that have the potential to succeed on the market. However, traditional methods of customer observation are time-consuming and labor-intensive. In this study, we propose a method that leverages the analysis of online customer reviews as a substitute for direct customer observations. By correlating a customer journey map (CJM) with online reviews, this research establishes a verb-centric analysis that produces a CJM based on online review data. Various text analysis techniques were utilized in this process. When applying online retail site review data, our method of customer observation required one week. This proved to be more efficient in comparison with traditional customer observation methods, which typically need at least one month to complete. Additionally, we observed that the customer behavior-based VOC (voice of customer) identified during the CJM mapping process offers broad insights that are distinct from traditional product feature-centric review analyses. This behavior VOC can be effectively utilized for product improvement, new product development, and product marketing. To verify the usefulness of the behavior VOC, we asked product development experts to evaluate the quantitative analysis results of the same reviews. The experts evaluated the CJM as useful for product conceptualization and selecting technology priorities.

show abstract

Section: Uniqueness and Contributionmentioning

confidence: 99%

Online Review Analysis from a Customer Behavior Observation Perspective for Product Development

Lee,

Chung,

Park

2024

Sustainability

View full text Add to dashboard Cite

show abstract

“…Machine learning and deep learning techniques have been widely applied in sentiment analysis to automate the process of sentiment classification. A notable resource that provides an in-depth exploration of these methods for sentiment analysis is the book referenced in [93,94].…”

Section: Sentiment Analysismentioning

confidence: 99%

A Brief Survey of Machine Learning and Deep Learning Techniques for E-Commerce Research

Zhang,

Guo,

Chen

et al. 2023

JTAER

View full text Add to dashboard Cite

The rapid growth of e-commerce has significantly increased the demand for advanced techniques to address specific tasks in the e-commerce field. In this paper, we present a brief survey of machine learning and deep learning techniques in the context of e-commerce, focusing on the years 2018–2023 in a Google Scholar search, with the aim of identifying state-of-the-art approaches, main topics, and potential challenges in the field. We first introduce the applied machine learning and deep learning techniques, spanning from support vector machines, decision trees, and random forests to conventional neural networks, recurrent neural networks, generative adversarial networks, and beyond. Next, we summarize the main topics, including sentiment analysis, recommendation systems, fake review detection, fraud detection, customer churn prediction, customer purchase behavior prediction, prediction of sales, product classification, and image recognition. Finally, we discuss the main challenges and trends, which are related to imbalanced data, over-fitting and generalization, multi-modal learning, interpretability, personalization, chatbots, and virtual assistance. This survey offers a concise overview of the current state and future directions regarding the use of machine learning and deep learning techniques in the context of e-commerce. Further research and development will be necessary to address the evolving challenges and opportunities presented by the dynamic e-commerce landscape.

show abstract

“…Current e-commerce research leverages advanced technologies such as augmented reality [ 26 , 27 ], electronic word-of-mouth (eWOM), review analysis, social media [ 28 , 29 , 30 , 31 ], haptic sensations, and voice shopping [ 3 , 32 , 33 ]. These technologies have been harnessed to enhance user engagement and personalize the shopping experience.…”

Section: Literature Reviewmentioning

confidence: 99%

Exploring the Impact of Time Spent Reading Product Information on E-Commerce Websites: A Machine Learning Approach to Analyze Consumer Behavior

Necula

2023

Behavioral Sciences

View full text Add to dashboard Cite

In this study, we aim to investigate the influence of the time spent reading product information on consumer behavior in e-commerce. Given the rapid growth of e-commerce and the increasing importance of understanding online consumer behavior, our research focuses on gaining a deeper understanding of customer navigation on e-commerce websites and its effects on purchasing decisions. Recognizing the multidimensional and dynamic nature of consumer behavior, we utilize machine learning techniques, which offer the capacity to handle complex data structures and reveal hidden patterns within the data, thereby augmenting our comprehension of underlying consumer behavior mechanisms. By analyzing clickstream data using Machine Learning (ML) algorithms, we provide new insights into the internal structure of customer clusters and propose a methodology for analyzing non-linear relationships in datasets. Our results reveal that the time spent reading product-related information, combined with other factors such as bounce rates, exit rates, and customer type, significantly influences a customer’s purchasing decision. This study contributes to the existing literature on e-commerce research and offers practical implications for e-commerce website design and marketing strategies.

show abstract

Online Reviews Sentiment Analysis and Product Feature Improvement with Deep Learning

Cited by 14 publications

References 21 publications

Online Review Analysis from a Customer Behavior Observation Perspective for Product Development

Online Review Analysis from a Customer Behavior Observation Perspective for Product Development

A Brief Survey of Machine Learning and Deep Learning Techniques for E-Commerce Research

Exploring the Impact of Time Spent Reading Product Information on E-Commerce Websites: A Machine Learning Approach to Analyze Consumer Behavior

Contact Info

Product

Resources

About