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- W2255994927 abstract "Many researchers have focused to develop a bankruptcy prediction model in terms of modeling techniques such as statistical methods or artificial intelligence techniques to secure enhanced performance. Most of the bankruptcy prediction models in academic studies have used financial ratios as input variables. However, the inclusion of qualitative information has not been actively discussed, despite the fact that using only the financial ratio has some drawbacks. This study proposes a bankruptcy prediction model using both quantitative information such as financial ratios and qualitative information acquired from economic news articles. We employ sentiment analysis among big data analytics techniques as a mechanism for processing qualitative information. We attempt to generate a domain-specific sentiment lexicon using economic news articles. The experimental results proved that incorporating qualitative information based on big data analytics to the traditional bankruptcy prediction model based on accounting information is effective for enhancing the predictive performance. The proposed model cannot only reflect relatively recent information but also environmental factors such as external economic conditions." @default.
- W2255994927 created "2016-06-24" @default.
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- W2255994927 date "2015-05-01" @default.
- W2255994927 modified "2023-09-23" @default.
- W2255994927 title "Bankruptcy Prediction Modeling Using Qualitative Information Based on Big data analytics" @default.
- W2255994927 hasPublicationYear "2015" @default.
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