User profile acquisition: A comprehensive framework to support personal information agents

Kasper, Gerrit; Braga, Diego de Siqueira; Martins, Denis Mayr Lima; Hellingrath, Bernd

doi:10.1109/la-cci.2017.8285719

Cited by 1 publication

(1 citation statement)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Consumer profiling, also called user labeling and user modeling, refers to the definition of users through a series of concise and differentiated attributes, descriptions, and labels. Generally, consumer profiles contain critical information that is used to identify an individual, including demographic and individual characteristics, such as age, name, knowledge, or expertise, as well as underlying characteristics, such as the user's preferences, interests, and behavior [83]. Previous research on consumer profiling has focused on the use of databases that contain digital traces of users, such as user behavior logs, page click history, and product transaction records.…”

Section: Consumer Profilingmentioning

confidence: 99%

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

Liu

Zhang

et al. 2022

Mathematics

View full text Add to dashboard Cite

In the Web2.0 era, user-generated content (UGC) provides a valuable source of data to aid in understanding consumers and driving intelligent business. Text mining techniques, such as semantic analysis and sentiment analysis, help to extract meaningful information embedded in UGC. However, research on text mining of UGC for e-commerce business applications involves interdisciplinary knowledge, and few studies have systematically summarized the research framework and application directions of related research in this field. First, based on e-commerce practice, in this study, we derive a general framework to summarize the mainstream research in this field. Second, widely used text mining techniques are introduced, including semantic and sentiment analysis. Furthermore, we analyze the development status of semantic analysis in terms of text representation and semantic understanding. Then, the definition, development, and technical classification of sentiment analysis techniques are introduced. Third, we discuss mainstream directions of text mining for business applications, ranging from high-quality UGC detection and consumer profiling, to product enhancement and marketing. Finally, research gaps with respect to these efforts are emphasized, and suggestions are provided for future work. We also provide prospective directions for future research.

show abstract

Section: Consumer Profilingmentioning

confidence: 99%