In recent years the development of machine learning and deep learning have been remarkably advanced due to the rapid growth of computer hardware In addition many studies have shown that deep learning techniques have achieved good results in the theme of Natural Language Processing In this study we apply the deep learning-based textual analysis to convert texts into sentiment scores which are then used measure CEO's sentiment to predict over-investment By doing so we can not only quantify texts into meaningful numbers but also make decision based on the analyzed results We found that the sentiment scores are positively related to over-investment Negative sentiment tends to have a greater impact on over-investment than positive sentiment Finally when multiplied by arousal valence would show more explanatory power
Date of Award | 2019 |
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Original language | English |
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Supervisor | Meng-Feng Yen (Supervisor) |
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Apply Deep Learning to Analyze CEO’s Sentiment and Predict Firms’ Overinvestment
哲維, ?. (Author). 2019
Student thesis: Doctoral Thesis