Using Data Mining Techniques for Going Concern Prediction-Sentiment Analysis of Media
碩士 === 國立臺灣大學 === 會計學研究所 === 107 === This study applies both data mining techniques(Random Forest and Support Vector Machine)and the traditional statistical method(i.e., Logistic Regression)to construct a going concern diagnostic model. This study also tries to assess the impact of media coverage on...
Main Authors: | Yi-Hsin Liao, 廖宜心 |
---|---|
Other Authors: | 林嬋娟 |
Format: | Others |
Language: | zh-TW |
Published: |
2019
|
Online Access: | http://ndltd.ncl.edu.tw/handle/6thfbm |
Similar Items
-
Construction of Going Concern Prediction Models by Using Data Mining Approach
by: WU, YI-JUN, et al.
Published: (2018) -
The application of data mining in going concern opinion
by: Jhong-Sian Yang, et al.
Published: (2009) -
Comparison of Sentiment Analysis against Digital Payment “T-cash and Go-pay” in Social Media Using Orange Data Mining
by: Novita Anggraini, et al.
Published: (2019-09-01) -
The Survival Analysis of Substantial Doubt about Going Concern
by: Hsin-Yi Tsai, et al.
Published: (2016) -
Opinion Mining on Social Media Data: Sentiment Analysis of User Preferences
by: Vasile-Daniel Păvăloaia, et al.
Published: (2019-08-01)