近年來因電腦硬體設備蓬勃發展讓機器學習及深度學習應用迅速崛起，除此之外亦有多項研究都表明，深度學習在自然語言處理(Natural Language Processing)的主題上有良好的成效。透過深度學習應用於文本分析將新聞媒體上的文字轉換成具體的情緒分數，並且將情緒分數運用於衡量公司經理人情緒，進而預測過度投資，此方法不只可以將文字量化成有意義的數字進行更多分析，更能夠利用分析結果進行企業分析和投資決策的依據。
因此本研究將深度學習技術應用於文本分析進而給予文字情緒分數，利用情緒分數指標代替經理人的情緒並預測過度投資。本研究發現經理人的情緒越正面時，也就是情緒分數越高會造成過度投資的增加，當經理人的情緒越負面時，也就是情緒分數越低也會造成過度投資的減少甚至投資不足，同時負向情緒對於過度投資的影響更勝於正向情緒，而研究也發現激發水準對於情緒向性存在著放大效果。

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.

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