本研究欲探究個股新聞影響台灣股票市場之關係，透過蒐集宏達電、台積電與鴻海等三間上市公司從2012年6月至2013年5月的歷史交易資料和個股新聞，使用文字探勘技術找出各新聞內容的特徵，再透過歷史資料、技術分析指標與kNN和2-way kNN演算法將新聞先做分類後分群，建立預測模型，分析新聞對股價漲跌的影響與程度，以及漲跌幅度較高之群集與股價漲跌和轉折的關係。 研究結果發現，加入技術分析指標後能夠提升分類的準確率，而漲跌類別內的分群能夠界定各群集與股價漲跌之間的關係，且漲跌幅度較高之群集的分析則能大幅提升投資準確率至80%左右，而股價轉折點之預測則能提供一個明確的投資進場時間點，並確保當投資人依照此預測模型的結果進行7交易日投資時，可以在風險極低的前提下，穩當且迅速的獲取2.82%至22.03%不等的投資報酬。 / This study investigated the relation that the stock news effect on Taiwan Stock Market. Through collected the historical transaction data and stock news from July, 2012 to May, 2013, and use text mining、kNN Classification and 2-Way kNN Clustering technique analyzing the stock news, build a forecast model to analyze the degree of news effect on the stock price, and find the relation between the cluster which has great degree and the reversal points of stock price. The result shows that using the change range and Technical Indicator rise classification’s accuracy, and clustering in the ”up” group and “down” group can identify the range stock price move, and rise the invested accuracy up to about 80 percent. The forecast of reversal points of stock price offers a specific time to invest, and insure the investors who execute a 7 trading day investment depend on this model can get 2.82 to 22.03 percent return reliably and quickly with low risk.