隨著資訊的數位化，數量龐大的文本數據能夠隨時取用。文字探勘（Text Mining）與情感分析（Sentiment Analysis）技術因而興起，各領域的研究人員都想從複雜的文本內容中，尋找有用的資訊，並發掘隱含在其中的洞見與價值。
　　本研究嘗試建立「時間序列預測股價之誤差」的修正模型。抓取中時新聞網2015/6/26至2020/7/30之11間公司的財經新聞共計68,775篇，藉由情感分析技術將每日的新聞情緒量化，再透過隨機森林迴歸演算法預測ARIMA模型預測股價時產生的誤差值，達到修正ARIMA模型的效果。
　　研究結果顯示，模型修正成功率介於36%-71%，平均為54%。整體而言模型修正成功率越高，MAPE下降率也越大。後續回溯公司的新聞內容與基本面分析，發現本研究只考慮單一公司的新聞對其股價影響，以致修正模型的預測效果並不穩定。或許加入更多相關廠商的新聞或產業趨勢文章加以分析，更能貼合大眾瀏覽財經新聞時產生的情緒，使修正模型具有一定程度能讓投資者作為投資決策的參考依據。

　　With the digitalization of information, a huge amount of text data can be captured at any time. Text mining and sentiment analysis have become more and more popular in recent years. Researchers in various fields want to apply these technologies to find useful information and insights from complicated text.
　　This research aimed to establish an error correction model of time series forecasting in stock price. With the base of 68,755 pieces of financial news from 11 companies from 26th June 2015 to 30th July 2020 captured from “chinatimes.com”, this model could quantify the daily news sentiment to predict the stock price error of the ARIMA model through random forest regression. Thus, it could achieve the effect of model correction.
　　The results revealed that the success rate of model correction ranges from thirty-six percent to seventy-one percent, with an average of fifty-four percent. Overall, the higher the success rate of model correction, the greater the rate of MAPE decrease. The results also showed that this model only considered the impact of a single company’s news on its stock price, so that the success rate of model correction was unstable. Perhaps adding more related news and industrial trend articles can fitter the public sentiment, so that the error correction model may serve as a guide for investors to make their investment decisions.

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