Text Mining of User-Generated Content (UGC) for Business Applications in E-Commerce: A Systematic Review
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 o...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-09-01
|
| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/10/19/3554 |
| _version_ | 1797478160970809344 |
|---|---|
| author | Shugang Li Fang Liu Yuqi Zhang Boyi Zhu He Zhu Zhaoxu Yu |
| author_facet | Shugang Li Fang Liu Yuqi Zhang Boyi Zhu He Zhu Zhaoxu Yu |
| author_sort | Shugang Li |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T21:29:01Z |
| format | Article |
| id | doaj.art-e50122300f1e4ba1b9a133c8740ccfd1 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-09T21:29:01Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-e50122300f1e4ba1b9a133c8740ccfd12023-11-23T21:03:31ZengMDPI AGMathematics2227-73902022-09-011019355410.3390/math10193554Text Mining of User-Generated Content (UGC) for Business Applications in E-Commerce: A Systematic ReviewShugang Li0Fang Liu1Yuqi Zhang2Boyi Zhu3He Zhu4Zhaoxu Yu5School of Management, Shanghai University, Shanghai 200444, ChinaSchool of Management, Shanghai University, Shanghai 200444, ChinaSchool of Management, Shanghai University, Shanghai 200444, ChinaSchool of Management, Shanghai University, Shanghai 200444, ChinaSchool of Management, Shanghai University, Shanghai 200444, ChinaDepartment of Automation, East China University of Science and Technology, Shanghai 200237, ChinaIn 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.https://www.mdpi.com/2227-7390/10/19/3554text mininguser-generated content (UGC)semantic analysissentiment analysisbusiness applicationsconsumer profiling |
| spellingShingle | Shugang Li Fang Liu Yuqi Zhang Boyi Zhu He Zhu Zhaoxu Yu Text Mining of User-Generated Content (UGC) for Business Applications in E-Commerce: A Systematic Review Mathematics text mining user-generated content (UGC) semantic analysis sentiment analysis business applications consumer profiling |
| title | Text Mining of User-Generated Content (UGC) for Business Applications in E-Commerce: A Systematic Review |
| title_full | Text Mining of User-Generated Content (UGC) for Business Applications in E-Commerce: A Systematic Review |
| title_fullStr | Text Mining of User-Generated Content (UGC) for Business Applications in E-Commerce: A Systematic Review |
| title_full_unstemmed | Text Mining of User-Generated Content (UGC) for Business Applications in E-Commerce: A Systematic Review |
| title_short | Text Mining of User-Generated Content (UGC) for Business Applications in E-Commerce: A Systematic Review |
| title_sort | text mining of user generated content ugc for business applications in e commerce a systematic review |
| topic | text mining user-generated content (UGC) semantic analysis sentiment analysis business applications consumer profiling |
| url | https://www.mdpi.com/2227-7390/10/19/3554 |
| work_keys_str_mv | AT shugangli textminingofusergeneratedcontentugcforbusinessapplicationsinecommerceasystematicreview AT fangliu textminingofusergeneratedcontentugcforbusinessapplicationsinecommerceasystematicreview AT yuqizhang textminingofusergeneratedcontentugcforbusinessapplicationsinecommerceasystematicreview AT boyizhu textminingofusergeneratedcontentugcforbusinessapplicationsinecommerceasystematicreview AT hezhu textminingofusergeneratedcontentugcforbusinessapplicationsinecommerceasystematicreview AT zhaoxuyu textminingofusergeneratedcontentugcforbusinessapplicationsinecommerceasystematicreview |