Text Mining of User-Generated Content (UGC) for Business Applications in E-Commerce: A Systematic Review

Li, Shugang; Liu, Fang; Zhang, Yuqi; Zhu, Boyi; Zhu, He; Yu, Zhensheng

doi:10.3390/math10193554

Cited by 20 publications

(12 citation statements)

References 127 publications

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“…UGC can be represented on a variety of platforms in different ways, referring to innovative text, picture, audio and video content, among which the textual content is the most popular (Roma and Aloini, 2019). Commentary content, as an important form of textual content, receives the widest possible attention nowadays (Li et al, 2022). Within the UGC paradigm, consumers express their opinions in the product recommendation process and promote the development of e-commerce socialization.…”

Section: Related Work 21 User-generated Contentmentioning

confidence: 99%

Is UGC sentiment helpful for recommendation? An application of sentiment-based recommendation model

Gao,

Wang,

Liu

2024

IMDS

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PurposeGiven the critical role of user-generated content (UGC) in e-commerce, exploring various aspects of UGC can aid in understanding user purchase intention and commodity recommendation. Therefore, this study investigates the impact of UGC on purchase decisions and proposes new recommendation models based on sentiment analysis, which are verified in Douban, one of the most popular UGC websites in China.Design/methodology/approachAfter verifying the relationship between various factors and product sales, this study proposes two models, collaborative filtering recommendation model based on sentiment (SCF) and hidden factors topics recommendation model based on sentiment (SHFT), by combining traditional collaborative filtering model (CF) and hidden factors topics model (HFT) with sentiment analysis.FindingsThe results indicate that sentiment significantly influences purchase intention. Furthermore, the proposed sentiment-based recommendation models outperform traditional CF and HFT in terms of mean absolute error (MAE) and root mean square error (RMSE). Moreover, the two models yield different outcomes for various product categories, providing actionable insights for organizers to implement more precise recommendation strategies.Practical implicationsThe findings of this study advocate the incorporation of UGC sentimental factors into websites to heighten recommendation accuracy. Additionally, different recommendation strategies can be employed for different products types.Originality/valueThis study introduces a novel perspective to the recommendation algorithm field. It not only validates the impact of UGC sentiment on purchase intention but also evaluates the proposed models with real-world data. The study provides valuable insights for managerial decision-making aimed at enhancing recommendation systems.

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Section: Related Work 21 User-generated Contentmentioning

confidence: 99%

Is UGC sentiment helpful for recommendation? An application of sentiment-based recommendation model

Gao,

Wang,

Liu

2024

IMDS

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“…Not all pieces of chatter are valuable for product/service improvement. The large amount of chatter that is not valuable leads to information overload (Li et al, 2022; Zhang et al, 2022). Thus, chatter classification is necessary.…”

Section: Literature Reviewmentioning

confidence: 99%

Capturing product/service improvement ideas from social media based on lead user theory

Yin,

Jiang,

Jain

et al. 2023

J of Product Innov Manag

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Capturing valuable product/service improvement ideas is helpful for the development of new features. However, the existing methods for capturing such improvement ideas have the disadvantages of high cost, long time lag, information overload, and difficulty in getting a response. We propose an innovative framework based on lead user theory for capturing product/service improvement ideas from user‐generated content on social media (henceforth called “chatter”). To identify the chatter containing improvement ideas, we design a machine‐learning‐based imbalanced classification model. Additionally, we use text summarization technology to get a rough sense of improvement ideas from the selected chatter. We validate the proposed framework by a case study in the automotive industry. The results demonstrate that the ideas extracted by our framework are breakthrough innovative, useful, feasible, and adoptable.

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“…User-generated content (UGC) refers to media content created by the general public rather than paid professionals, and it is primarily distributed online. UGC is easily accessed by users in real-time via the Internet [7,23]. UGC takes various forms, including Twitter tweets, Facebook status updates, YouTube videos, and consumer-generated product reviews, among others [24].…”

Section: Effect Of User-generated Content On Online Salesmentioning

confidence: 99%

“…These are assessments and descriptions of a product or company, generated and shared by current or former consumers, conveying positive or negative feedback in e-commerce platforms [31]. Amazon, eBay, Tmall, and JD are renowned e-commerce platforms that attract numerous users and offer diverse product selections, resulting in a significant amount of UGC [23]. Consumers generally perceive such reviews as crucial and trustworthy sources of information that significantly influence their purchasing behavior [32,33].…”

Section: Effect Of User-generated Content On Online Salesmentioning

confidence: 99%

“Customer Reviews or Vlogger Reviews?” The Impact of Cross-Platform UGC on the Sales of Experiential Products on E-Commerce Platforms

Jia,

Feng,

Wang

et al. 2023

JTAER

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User-generated content (UGC) from e-commerce platforms and third-party platforms can impact customer-perceived risk and influence product sales in online stores. However, the understanding of UGC from which platform type yields a stronger effect on product sales and how the effects interact across the platforms remains limited. This limitation arises from the complexity of consumer purchasing behavior and information processing, as well as the heterogeneity of UGC features across different platforms and the uncertainty surrounding causal relationships. This study constructs a novel cross-platform framework using the elaboration likelihood model (ELM) to investigate the underlying mechanism of how cross-platform UGC affects online sales of experiential products. Additionally, it examines the mediating effect of purchase intention in the relationship between cross-platform UGC and product sales, as well as the moderating effect of product price. Taking the e-commerce platform Tmall and third-party platform Bilibili as a cross-platform example, we analyzed customer reviews on Tmall and vlogger reviews on Bilibili for 300 cosmetic products, using text sentiment analysis and multiple regression. Results show that the number of product evaluations from third-party platforms positively impacts sales, but this impact is weaker compared to the influence of UGC originating from e-commerce platforms on sales. The underlying mechanism refers to the process by which UGC on an e-commerce platform directly impacts sales and also influences sales through purchase intention. In contrast, UGC on third-party platforms only influences sales through purchase intention. Furthermore, the product price has no significant moderating effect on the positive relationship between review length and sales. This study provides a cross-platform UGC research framework that can guide effective cross-platform marketing management by shedding light on the role of UGC in reducing customer-perceived risk and its impact on online sales of experiential products.

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Text Mining of User-Generated Content (UGC) for Business Applications in E-Commerce: A Systematic Review

Cited by 20 publications

References 127 publications

Is UGC sentiment helpful for recommendation? An application of sentiment-based recommendation model

Is UGC sentiment helpful for recommendation? An application of sentiment-based recommendation model

Capturing product/service improvement ideas from social media based on lead user theory

“Customer Reviews or Vlogger Reviews?” The Impact of Cross-Platform UGC on the Sales of Experiential Products on E-Commerce Platforms

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