UGC Knowledge Features and Their Influences on the Stock Market: An Empirical Study Based on Topic Modeling
According to the natural language perspective, UGC has been significantly used for the screening of key nodes in knowledge discovery and strategic investment. This article presents a new research framework that is proposed for the decomposition of UGC knowledge feature extraction into topic recognit...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Information |
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| Online Access: | https://www.mdpi.com/2078-2489/13/10/454 |
| _version_ | 1797440846560231424 |
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| author | Ning Li Kefu Chen Huixin He |
| author_facet | Ning Li Kefu Chen Huixin He |
| author_sort | Ning Li |
| collection | DOAJ |
| description | According to the natural language perspective, UGC has been significantly used for the screening of key nodes in knowledge discovery and strategic investment. This article presents a new research framework that is proposed for the decomposition of UGC knowledge feature extraction into topic recognition and language analysis, mainly. For visual analysis of associated topics, the LDAvis approach is utilized. Then, risk features of UGC knowledge are assigned according to language attribution. Based on previous studies, the risk attribute lexicon is further updated by judging semantic distance through word vectors. This research uses platform data and individual stock data as samples for subject recognition and knowledge feature extraction. A regression model is constructed based on the panel data after natural language processing to verify the feedback effect of the market at strategic risk measurement. It can be found from the conclusion that the change in market behavior is regular and correlates with the change in the UGC risk degree of individual stocks. The purpose of this paper is to examine the value of UGC in investment decision-making from the perspective of knowledge discovery. The research content can provide a reference for data mining, fintech, strategic risk monitoring, and other related works. |
| first_indexed | 2024-03-09T12:15:19Z |
| format | Article |
| id | doaj.art-df9fdf6370d94282851f8f5b532be3ba |
| institution | Directory Open Access Journal |
| issn | 2078-2489 |
| language | English |
| last_indexed | 2024-03-09T12:15:19Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj.art-df9fdf6370d94282851f8f5b532be3ba2023-11-30T22:48:11ZengMDPI AGInformation2078-24892022-09-01131045410.3390/info13100454UGC Knowledge Features and Their Influences on the Stock Market: An Empirical Study Based on Topic ModelingNing Li0Kefu Chen1Huixin He2School of Communication, Fujian Normal University, Fuzhou 350007, ChinaManagement School, Xiamen University, Xiamen 361000, ChinaComputer Science and Engineering School, Huaqiao University, Xiamen 361021, ChinaAccording to the natural language perspective, UGC has been significantly used for the screening of key nodes in knowledge discovery and strategic investment. This article presents a new research framework that is proposed for the decomposition of UGC knowledge feature extraction into topic recognition and language analysis, mainly. For visual analysis of associated topics, the LDAvis approach is utilized. Then, risk features of UGC knowledge are assigned according to language attribution. Based on previous studies, the risk attribute lexicon is further updated by judging semantic distance through word vectors. This research uses platform data and individual stock data as samples for subject recognition and knowledge feature extraction. A regression model is constructed based on the panel data after natural language processing to verify the feedback effect of the market at strategic risk measurement. It can be found from the conclusion that the change in market behavior is regular and correlates with the change in the UGC risk degree of individual stocks. The purpose of this paper is to examine the value of UGC in investment decision-making from the perspective of knowledge discovery. The research content can provide a reference for data mining, fintech, strategic risk monitoring, and other related works.https://www.mdpi.com/2078-2489/13/10/454UGCnatural language analysisLDA topic modelknowledge featuresmarket impact |
| spellingShingle | Ning Li Kefu Chen Huixin He UGC Knowledge Features and Their Influences on the Stock Market: An Empirical Study Based on Topic Modeling Information UGC natural language analysis LDA topic model knowledge features market impact |
| title | UGC Knowledge Features and Their Influences on the Stock Market: An Empirical Study Based on Topic Modeling |
| title_full | UGC Knowledge Features and Their Influences on the Stock Market: An Empirical Study Based on Topic Modeling |
| title_fullStr | UGC Knowledge Features and Their Influences on the Stock Market: An Empirical Study Based on Topic Modeling |
| title_full_unstemmed | UGC Knowledge Features and Their Influences on the Stock Market: An Empirical Study Based on Topic Modeling |
| title_short | UGC Knowledge Features and Their Influences on the Stock Market: An Empirical Study Based on Topic Modeling |
| title_sort | ugc knowledge features and their influences on the stock market an empirical study based on topic modeling |
| topic | UGC natural language analysis LDA topic model knowledge features market impact |
| url | https://www.mdpi.com/2078-2489/13/10/454 |
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