雖然效率市場假說強調股票的市場價格無法預測，行為財務理論卻指出效率市場假說未必合理，股票的漲跌走勢亦成為財金預測建模的重要議題。隨著資訊科技的進步，投資人的市場訊息來源亦更加多元，新聞及社群媒體所夾帶的大量市場訊息亦可能影響投資判斷，進而造成股市波動。本研究透過建立股市專用情緒字典以量測新聞文章或是網路輿論所蘊含的情緒，利用文字探勘之主題分析擷取文章或輿論背後的主題，並結合情緒和主題形成新的預測指標，最終納入財經及股市技術指標進行機器學習預測。本研究透過PTT Stock看板與台灣股市資料驗證本研究提出方法之有效性，實證結果指出，納入情緒及主題分析後，股票漲跌趨勢預測之正確率有所提升。本研究所提出之分析架構，包括自訂專用情緒字典並結合主題分析，可以更精確掌握文本中的重要面向，未來亦可延伸應用於其他領域，以提升預測及決策品質。

Although efficient market hypothesis states that the asset prices reflect all available information and thus stock market fluctuation is unpredictable, behavioral finance takes a different viewpoint and consider that the stock prices can be influenced by psychological biases and behaviors. With the advancement of information technology, investors nowadays can access diversified sources of market information. The market information carried by news and social media thus may affect investment judgment and cause stock market fluctuation. In this study, we build a stock market sentiment dictionary to measure the sentiment in news articles or online opinions, use topic modeling to extract the topics behind the articles or opinions, and combine the sentiment associated with the extracted topics into new indicators, which are eventually incorporated with financial and stock market technical indicators for machine learning prediction. The effectiveness of the present study is verified by PTT Stock information and Taiwan stock market data. The results show that the combining of sentiment and topic analysis can improve the prediction accuracy. The analysis framework of the present study, which includes a customized sentiment dictionary and topic analysis, can be applied in other fields to master the important information in text, and improve the quality of prediction and decision making.

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