博碩士論文 etd-0807116-200608 詳細資訊


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姓名 黃新晴(Hsin-Ching Huang) 電子郵件信箱 E-mail 資料不公開
畢業系所 資訊管理學系研究所(Information Management)
畢業學位 碩士(Master) 畢業時期 104學年第2學期
論文名稱(中) 從財金新聞利用時間序列預測公司財報之營收走勢
論文名稱(英) Time Series forecast of Company Revenue Trend Using Financial News
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    紙本論文:5 年後公開 (2021-09-07 公開)

    電子論文:使用者自訂權限:校內 3 年後、校外 3 年後公開

    論文語文/頁數 英文/41
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    摘要(中) 現今文字探勘的應用範疇非常廣泛,而本研究專注於財金領域。近來許多財金領域的研究多數著重在股票市場漲跌趨勢或是股價的預測,少數研究財務報表上重要的經濟指標,進而了解公司的金流與市場占有率等財務現況;其中多數研究即是藉由網路上大量的文字資料進行情緒分析來獲得有用的資訊。
    本研究利用自動化擴充的財金情緒字典,將財金新聞進行情緒分析,同時結合自回歸移動平均模型將公司營收進行時間序列分析。實驗結果顯示,以一季的新聞文章預測該季的營收走勢可達將近84%的準確度。
    摘要(英) The application nowadays of text mining are very extensive, and our study focuses on the field of finance. Most recent text mining studies of finance research the prediction of the stock market trend or the forecast of the stock prices, and other important economic indicators of the companies, as appeared in their financial statements, are seldom addressed. Yet these indicators could be quite important and reflect the financial status of the companies’ cash flow and market share. Most of these studies use the large amount of text on the Internet and obtain useful information through sentiment analysis.
    In this study, we adopt an automatically expanded finance sentiment dictionary and aggregate the sentiment values of the financial news. Furthermore, we combine text mining with the ARIMA model for time series analysis of the company's revenue. Experimental result shows that the forecasting quarterly revenue trend from analyzing news articles appeared in the last quality is quite effective with the precision up to 84%.
    關鍵字(中)
  • 自回歸移動平均模型
  • 情緒分析
  • 財金新聞
  • 時間序列
  • 營收預測
  • 關鍵字(英)
  • sentiment analyze
  • financial news
  • revenue prediction
  • time series
  • ARIMA model
  • 論文目次 論文審定書 i
    摘 要 ii
    Abstract iii
    CHAPTER 1 – Introduction 1
    1.1 Background and Motivation 1
    1.2 Research Purpose 2
    1.3 Thesis Organization 2
    CHAPTER 2 – Literature Review 4
    2.1 Financial Aspect 4
    2.1.1 The Importance of Revenue 4
    2.1.2 News Impact Financial Markets 6
    2.2 Technical Aspect 7
    2.2.1 Text Mining Approach 7
    2.2.2 Time Series Approach 8
    CHAPTER 3 – The Research Process 10
    3.1 Research Skeleton 10
    3.2 Data Crawling 11
    3.3 News/Event Schema Design 11
    3.4 Lexicon Construction 14
    3.4.1 Entity Lexicon 14
    3.4.2 Aspect Lexicon 15
    3.4.3 Source Lexicon 16
    3.4.4 Sentiment Lexicon 16
    3.4.5 Past-time Lexicon 17
    3.5 Sentiment Analysis 18
    3.6 Prediction Model Construction 19
    3.6.1 ARIMA Model 19
    3.6.2 Prediction Model 19
    CHAPTER 4 – Evaluation 22
    4.1 Dataset Construction 22
    4.2 Experiment Design 23
    4.3 Experiment Results 25
    CHAPTER 5 – Conclusion 30
    5.1 Implication 30
    5.2 Limitation 30
    5.3 Future Work 31
    References 32
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    口試委員
  • 楊婉秀 - 召集委員
  • 蔡維哲 - 委員
  • 黃三益 - 指導教授
  • 口試日期 2016-07-07 繳交日期 2016-09-07

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