<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>in the network technology increasingly innovation, weibo social media such as WeChat gradually become people thought together and important platform of communication, both theoretical knowledge and practical case studies, have proved that social media can be intuitive show people the focus of the current problems, to provide effective basis for market development and investment decisions. Especially in the comprehensive promotion of text mining technology, topic extraction related to corporate social media has been reasonably applied in the financial market. However, due to the problems of sparse and unstructured data, topic extraction in practical application has become the main subject explored by researchers and scholars. This paper studies enterprise microblogging social media, for example, using both the theme of the text content and platform interactive method, according to the relations between the two kinds of typical weibo relation and the matrix, and by using two consecutive nonnegative matrix decomposition process is analyzed, finally the corresponding distance as a result, and extracting keywords to express the relationship. The actual verification results show that the clustering accuracy and theme similarity of this method are more effective than traditional theme extraction methods in corporate social media.</p>
</abstract>

in the network technology increasingly innovation, weibo social media such as WeChat gradually become people thought together and important platform of communication, both theoretical knowledge and practical case studies, have proved that social media can be intuitive show people the focus of the current problems, to provide effective basis for market development and investment decisions. Especially in the comprehensive promotion of text mining technology, topic extraction related to corporate social media has been reasonably applied in the financial market. However, due to the problems of sparse and unstructured data, topic extraction in practical application has become the main subject explored by researchers and scholars. This paper studies enterprise microblogging social media, for example, using both the theme of the text content and platform interactive method, according to the relations between the two kinds of typical weibo relation and the matrix, and by using two consecutive nonnegative matrix decomposition process is analyzed, finally the corresponding distance as a result, and extracting keywords to express the relationship. The actual verification results show that the clustering accuracy and theme similarity of this method are more effective than traditional theme extraction methods in corporate social media.

Study on Interactive Relations between Enterprise Social Media and Decision Style Based on a vector Autoregressive Model

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

in the network technology increasingly innovation, weibo social media such as WeChat gradually become people thought together and important platform of...

Study on Interactive Relations between Enterprise Social Media and Decision Style Based on a vector Autoregressive Model

Online veröffentlicht: 15. Juli 2022

Seitenbereich: 881 - 892

Eingereicht: 19. Feb. 2022

Akzeptiert: 21. Apr. 2022

DOI: https://doi.org/10.2478/amns.2022.2.0075

Schlüsselwörter
Put here your keywords vector autoregressive model, Corporate social media, Interactive relationship, Topic extraction, Relationship matrix

© 2023 Jinrong Mao et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Study on Interactive Relations between Enterprise Social Media and Decision Style Based on a vector Autoregressive Model

Online veröffentlicht: 15. Juli 2022

Seitenbereich: 881 - 892

Eingereicht: 19. Feb. 2022

Akzeptiert: 21. Apr. 2022

DOI: https://doi.org/10.2478/amns.2022.2.0075

SchlüsselwörterPut here your keywords vector autoregressive model, Corporate social media, Interactive relationship, Topic extraction, Relationship matrix

© 2023 Jinrong Mao et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Put here your keywords vector autoregressive model, Corporate social media, Interactive relationship, Topic extraction, Relationship matrix