現今網路新聞媒體崛起，網路新聞成為投資人執行投資決策的判斷依據之一，然而財經新聞網站每天所產生的大量新聞資訊，投資人已經無法利用傳統人力閱讀篩選的方式，逐一判斷每篇新聞所反映目前市場的情緒。
　　因此本研究為協助投資人快速了解目前市場情緒，以財經新聞媒體Anue鉅亨網的台灣股市新聞進行中文新聞文本情緒分類，使用文字探勘與文本分類的技術，探討中文文本的資料前處理與文本分類模型的成效，觀察不同的特徵提取與挑選方法搭配不同分類模型的效果為何。其中字詞特徵提取與挑選使用的方法為N-gram、TF-IDF、卡方與互資訊，而分類模型使用樸素貝葉斯分類器、fastText與多層感知器。
　　最後研究結果指出：(1).N-gram字詞特徵提取在三種分類模型中皆能提升準確率，特別是應用在樸素貝葉斯分類器，可以有效地改善模型假設字詞特徵獨立的缺點。(2).TF-IDF字詞特徵挑選僅對樸素貝葉斯分類器有效，可以在減少字詞數量的情況下，同時降低訓練時間與提升準確度。(3).卡方檢定與互資訊特徵挑選皆可以提升fastText與多層感知器的準確率，且卡方檢定特徵挑選與fastText分類模型取得本研究最優異的分類效果。

　　Nowadays, online news has become one of the judgments for investors to make investment decisions. However, a large amount of information generated by financial news websites everyday makes investors unable to use traditional human reading and screening methods to judge and verify the current market sentiment reflected by each news report.
　　In order to help investors understand the current market sentiment quickly, we use the techniques of text mining and text classification to classify new sentiments. This study collects Taiwanese stock market news of Anue Financial News and use different methods of text pre-processing and classifier to achieve the best classification performance.
　　The empirical results show：(1) N-gram feature extraction can improve the accuracy of all classifiers, especially the naive Bayes classifier which can effectively overcome shortcomings of the independence assumptions. (2) TF-IDF feature selection only effective for naive Bayes classifier. Under the circumstances of the number of words decreasing, it can improve the accuracy and reduce the training time. (3) The Chi-square test and mutual information feature selection can improve the accuracy of both fastText and Multi-layer Perceptron. Furthermore, the combination of Chi-square test feature and fastText achieved the best performance in this study.

論文審定書 i
摘要 ii
Abstract iii
目錄 iv
圖次 vi
表次 vii
第一章 緒論 1
第一節 研究動機與目的 1
第二節 研究架構 2
第二章 文獻回顧 3
第一節 文字探勘 3
第二節 文本分類 5
第三章 研究方法與模型建立 9
第一節 實驗架構 9
第二節 資料蒐集與建立 10
第三節 前處理與特徵挑選 11
第四節 新聞文本分類器 24
第五節 分類表現評估標準 28
第四章 實證結果 29
第一節 資料敘述 29
第二節 分類表現評估 32
第五章 結論與建議 38
第一節 結論 38
第二節 未來研究建議 38
參考文獻 40

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