相關研究指出，新聞文字的情緒一直以來是扮演可能影響金融市場波動的角色之一。所以在每天千以萬計的新聞訊息之中，相較於傳統手工的方式，能夠有效快速地處理文字內容並且進行輿論導向分析，對市場交易走勢的後續預測與判斷是有幫助的。使用人工分類其缺點為成本高。故近年來，許多學者提出各種不同的新聞情緒分類器。在這些研究當中，大多利用新聞全文進行關鍵字的擷取並建立分類模型。因利用新聞全文之關鍵字進行新聞情緒判定會降低準確度，故本論文提出一個以句子為基礎的中文新聞情緒分類演算法。所提方法首先將收集的新聞進行斷句。接著，使用 TextRank 與 Word2Vec 進行關鍵句子的判定。所找出的關鍵句子進一步先透過財金新聞詞典進行情緒分數的計算用以判定每篇新聞的正負面情緒標籤，再用於產生句子的關鍵字，進而形成可用之訓練資料集。最後，透過長短期記憶深度學習與遞歸神經網路模型建立分類器。實驗數據部份，透過選用台灣二家股票公司近五年資料進行評估，結果顯示所提的方法是有效的。

The literature indicates that news sentiment always has impact on the financial market. Thus, if news can be analyzed effectively, it will have benefit for the following trading. Traditionally, news sentiment is annotated by human, and the cost is high. Therefore, many researchers proposed different approaches for building classifiers for sentiment classification. Most of them are extracting key words from news, and the extracted  key news are utilized to construct classifiers. However, using key words extracted from news may reduce the accuracy of the model, this thesis thus proposed a sentence-based Chinese news sentiment classification. It first divides news into sentences. Then, using the TextRank and Word2Vec, the key  sentences are generated. By using the generated key sentences, the sentiment scores of them are calculated by comparing the existing financial lexicon to determine positive or negative sentiment of news, and the key words are also generated from key sentences to form training data set. At last, the Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) are used to construct the news sentiment classification models. Experiments on a five-year real data set of two companies at Taiwan were made to evaluate the proposed models. The results indicate that the proposed approaches are effective.

第一章	緒論	1
1.1	研究動機	1
1.2	研究目的	3
1.3	讀者指南	3
第二章	文獻探討與背景知識	4
2.1	英文新聞情緒分類技術	4
2.2	中文新聞情緒分類技術	4
2.3	分類模型	5
2.3.1	SVM	5
2.3.2	CNN	6
2.3.3	RNN	7
2.3.4	LSTM	8
2.3.5	GRU	12
2.4	文字前處理技術	13
2.4.1	Word2Vec 模型	13
2.4.2	TextRank 演算法	15
第三章	基於深度句子的中文情感分類	17
3.1	系統架構圖	17
3.2	資料預處理	18
3.2.1	提取關鍵句子與字詞	18
3.2.2	文本情感分析	19
3.2.3	關鍵詞轉成特徵值清單	20
3.3	建立分類模型	21
3.3.1	訓練模型	22
3.3.2	測試模型	22
3.4	虛擬碼	23
3.5	流程範例	25
第四章	實驗分析與結果	33
4.1	實驗資料蒐集	33
4.1.1	網路新聞資料蒐集	33
4.1.2	正負面詞庫蒐集	33
4.1.3	Word to Vector 模型初始化資料蒐集	33
4.2	實驗環境設定	34
4.3	不同資料的時間區間對模型準確度的影響	34
4.4	關鍵句子數量對模型準確度的影響	35
4.5	關鍵字數量對正負面新聞分類的影響與模型準確度	36
第五章 結論與改進	38
參考文獻	39
附錄 英文論文	43


圖目錄
圖 1情緒分析技術組識圖	2
圖 2 CNN主流卷積網路LeNet-5 架構圖	6
圖 3遞迴類神經網路結構圖	7
圖 4長短記憶模型	8
圖 5長期記憶	9
圖 6遺忘閘門	9
圖 7輸入閘門	10
圖 8新舊狀態更迭	11
圖 9輸出閘門	11
圖 10 Gated Recurrent Unit (GRU)	13
圖 11 CBOW 與 Skip-grap 模型架構圖	15
圖 12以句子為基礎的中文情感分類器流程圖	17
圖 13新聞關鍵句提取流程圖	18
圖 14 決定新聞文章正負面情緒流程圖	19
圖 15關鍵詞轉成特徵值清單流程	21
圖 16 使用TextRank演算法找出關鍵字	25
圖 17使用特定標點符號對文章進行斷句得到句子集合	26
圖 18使用Jieba將句子進行斷詞	26
圖 19 第一篇文章關鍵詞轉換成固定長度的特徵值清單陣列	30
圖 20 模型初始化總覽	31
圖 21模型訓練與驗證之精確度	32
圖 22模型訓練與驗證之損失分數	32

表 1模型各神經網路層參數說明	21
表 2 模型訓練的方式	22
表 3 訓練步驟參數說明	22
表 4 關鍵句子提取的虛擬碼	23
表 5 情緒分析的虛擬碼	23
表 6 關鍵詞轉換特徵值清單的虛擬碼	24
表 7 Textrank關鍵詞與Jieba斷詞的一般詞進行相似度比較表	26
表 8 文章中每一句子重要程度得分	27
表 9 Top-k句子與Title選取得到文章的關鍵句	27
表 10新聞文章關鍵句子	28
表 11關鍵句子使用Jieba斷詞得到 KSWi	28
表 12正負情緒字典清單範例	29
表 13 KSWi與正負面情緒字典清單比較得到詞的傾向分數	29
表 14 WOSi加總起來得到每個關鍵句SOS之情緒分數	30
表 15 實驗環境軟硬體工具清單	34
表 16 不同訓練與測試資料的時間區間筆數	34
表 17不同訓練與測試資料的時間區間之實驗結果	35
表 18關鍵句的數量對模型準確度影響	35
表 19 不同關鍵字的個數對正負面新聞分類的影響	36
表 20不同關鍵字的個數對模型精確度影響	36

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