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

<p>It has become a trend for enterprises to establish financial shared service centers for digital financial transformation, but the emotional impact of this transformation on the original financial staff of enterprises has been neglected. In this paper, we collect relevant intranet texts of enterprises, mine the emotions of the collected texts through the LDA theme algorithm, use the improved LSTM model to classify and identify the emotions of the collected texts, and construct the LDA-BiLSTM sentiment analysis model to analyze the anxiety condition of the financial staff of enterprises. The efficiency of LDA can be improved by using the weighted median to deal with outliers. Two LSTM networks are combined to form a BiLSTM network to improve the problem of inaccurate judgment of sentiment indicators by a single LSTM. Finally, the model is used to empirically analyze the emotions of financial staff before and after the transformation of an enterprise’s financial digital service. Negative feelings that are linked to anxiety have an accuracy rate of 95.23%. The frequency of separation after the transition was as high as 0.5, and the frequency of dismissal was as high as 0.423. Overall sentiment scores were lower than 0.7. After the transition, the number of people worried about negative feelings related to anxiety rose from 32% to 64%. Finance professionals are experiencing a significant increase in anxiety due to the post-transition of the enterprise.</p>
</abstract>

It has become a trend for enterprises to establish financial shared service centers for digital financial transformation, but the emotional impact of this transformation on the original financial staff of enterprises has been neglected. In this paper, we collect relevant intranet texts of enterprises, mine the emotions of the collected texts through the LDA theme algorithm, use the improved LSTM model to classify and identify the emotions of the collected texts, and construct the LDA-BiLSTM sentiment analysis model to analyze the anxiety condition of the financial staff of enterprises. The efficiency of LDA can be improved by using the weighted median to deal with outliers. Two LSTM networks are combined to form a BiLSTM network to improve the problem of inaccurate judgment of sentiment indicators by a single LSTM. Finally, the model is used to empirically analyze the emotions of financial staff before and after the transformation of an enterprise’s financial digital service. Negative feelings that are linked to anxiety have an accuracy rate of 95.23%. The frequency of separation after the transition was as high as 0.5, and the frequency of dismissal was as high as 0.423. Overall sentiment scores were lower than 0.7. After the transition, the number of people worried about negative feelings related to anxiety rose from 32% to 64%. Finance professionals are experiencing a significant increase in anxiety due to the post-transition of the enterprise.

Exploring the Impact of Digital Shared Services in Finance on Corporate Treasurers’ Job Anxiety

Applied Mathematics and Nonlinear Sciences

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

It has become a trend for enterprises to establish financial shared service centers for digital financial transformation, but the emotional impact of this...

Exploring the Impact of Digital Shared Services in Finance on Corporate Treasurers’ Job Anxiety

Online veröffentlicht: 31. Jan. 2024

Seitenbereich: -

Eingereicht: 20. Dez. 2023

Akzeptiert: 30. Dez. 2023

DOI: https://doi.org/10.2478/amns-2024-0205

Schlüsselwörter
LDA topic algorithm, BiLSTM, Weighted median, Sentiment analysis, Finance digital shared services

© 2024 Yuying Guo, published by Sciendo

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

Exploring the Impact of Digital Shared Services in Finance on Corporate Treasurers’ Job Anxiety

Online veröffentlicht: 31. Jan. 2024

Seitenbereich: -

Eingereicht: 20. Dez. 2023

Akzeptiert: 30. Dez. 2023

DOI: https://doi.org/10.2478/amns-2024-0205

SchlüsselwörterLDA topic algorithm, BiLSTM, Weighted median, Sentiment analysis, Finance digital shared services

© 2024 Yuying Guo, published by Sciendo

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

Schlüsselwörter
LDA topic algorithm, BiLSTM, Weighted median, Sentiment analysis, Finance digital shared services