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    題名: 建構GDELT數位新聞分析流程於Spark大數據平台:以新聞事件影響力探究美國S&P股市指數變化為例
    Establishing GDELT digital news analytics pipeline on the Spark platform : exploiting news events influences on S&P stock index variations as an example
    作者: 黃書瑋
    Huang, Shu Wei
    貢獻者: 胡毓忠
    Hu, Yuh Jong
    黃書瑋
    Huang, Shu Wei
    關鍵詞: GDELT專案
    滾動式機器學習
    大數據分析流程
    新聞影響力
    亞馬遜網路服務
    GDELT project
    Rolling-Window machine learning
    Big data analysis pipeline
    News events influences
    AWS
    日期: 2017
    上傳時間: 2017-08-10 10:18:59 (UTC+8)
    摘要: 於2013年正式公開的GDELT專案號稱能監控全球65種發行語言的數位新聞媒體,利用現今完善的機器學習演算法、自然語言處理及深度學習等先進人工智慧技術,將寶貴的新聞資料,萃取與轉換成具有58組欄位資訊的結構化資料,提供各領域進一步研究與應用。本研究以GDELT新聞事件資料集來開發大數據資料分析流程,並且利用Spark ML Pipeline的技術,在亞馬遜網路服務(AWS)的雲端平台上,完成以滾動式機器學習演算法,來進行以GDELT資料為主的美國標普500(S&P 500)股市指數追蹤,與特定「佔領華爾街」事件影響力的因果分析。本研究所採用的45天滾動式隨機森林模型,在歷史指數的追蹤與預測表現上,獲得了方均根差僅43.35(誤差2.12%)的優異成果;於雲端系統上的15分鐘近即時滾動式預測誤差,更是低於1.5%。在因果分析方面,本研究採用貝氏時間序列模型分析「佔領華爾街」事件影響股市的反事實指數,闡釋該事件的發生與後續效應,促使S&P 500股市指數在觀察區間中上漲116.76點。
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    描述: 碩士
    國立政治大學
    資訊科學系碩士在職專班
    104971002
    資料來源: http://thesis.lib.nccu.edu.tw/record/#G0104971002
    資料類型: thesis
    顯示於類別:[資訊科學系碩士在職專班] 學位論文

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