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題名 應用情感分析於股票趨勢預測 -以台灣人工智慧(AI)概念股為例
Sentiment Analysis in Taiwan Artificial Intelligence Concept Stock
作者 曾梓閑
Tseng, Zi-Xian
貢獻者 姜國輝<br>季延平
Jiang, Guo Hui<br>Chi, Yan Ping
曾梓閑
Tseng, Zi-Xian
關鍵詞 情感分析
文字探勘
機器學習
時間序列分析
人工智慧概念股
日期 2018
上傳時間 13-Jul-2018 15:16:47 (UTC+8)
摘要 近年來人工智慧的發展及應用備受各界關注，隨著人工智慧發展大躍進，AI概念股在這波浪潮下因運而生，而2017年AI類股相關指數漲贏美股大盤，人工智慧發展對我國也帶來了重要的影響，本研究中對台灣人工智慧概念股做一個初步的調查，並依據技術面與應用面定義出29支台灣AI概念股作為本研究之研究範圍。
本研究利用財經新聞作為情感分析的來源，計算出情緒指數後利用時間序列分析找出與情緒指數相關的指標，再建立分類模型來預測股票的漲跌。經實證結果發現，情緒指數與人工智慧概念股股價走勢有其影響，推測散戶看到新聞報導後會受到新聞文本的影響進而影響股價走勢，並於2~3 天影響最為明顯。
另外，本研究實驗結果中也發現結合情緒指數與技術指標建立的分類模型優於單純以技術指標分類模型來預測股價的漲跌趨勢。而加入其他間接指標如國際指標、總體經濟指標、台股資訊指標建立的分類模型優於結合情緒指數與技術指標建立的分類模型，整體分類準確度達82%。
在結合情緒指數與技術指標的分類模型上，以分類器的準確度及召回率效果而言，KNN的準確率為0.8 及召回率為0.67的分類結果較為優異，以F1-measure而言則是0.65 的隨機森林效果較為優異。
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描述 碩士
國立政治大學
資訊管理學系
1053560163
資料來源 http://thesis.lib.nccu.edu.tw/record/#G1053560163
資料類型 thesis
dc.contributor.advisor 姜國輝<br>季延平zh_TW
dc.contributor.advisor Jiang, Guo Hui<br>Chi, Yan Pingen_US
dc.contributor.author (Authors) 曾梓閑zh_TW
dc.contributor.author (Authors) Tseng, Zi-Xianen_US
dc.creator (作者) 曾梓閑zh_TW
dc.creator (作者) Tseng, Zi-Xianen_US
dc.date (日期) 2018en_US
dc.date.accessioned 13-Jul-2018 15:16:47 (UTC+8)-
dc.date.available 13-Jul-2018 15:16:47 (UTC+8)-
dc.date.issued (上傳時間) 13-Jul-2018 15:16:47 (UTC+8)-
dc.identifier (Other Identifiers) G1053560163en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/118643-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 資訊管理學系zh_TW
dc.description (描述) 1053560163zh_TW
dc.description.abstract (摘要) 近年來人工智慧的發展及應用備受各界關注，隨著人工智慧發展大躍進，AI概念股在這波浪潮下因運而生，而2017年AI類股相關指數漲贏美股大盤，人工智慧發展對我國也帶來了重要的影響，本研究中對台灣人工智慧概念股做一個初步的調查，並依據技術面與應用面定義出29支台灣AI概念股作為本研究之研究範圍。
本研究利用財經新聞作為情感分析的來源，計算出情緒指數後利用時間序列分析找出與情緒指數相關的指標，再建立分類模型來預測股票的漲跌。經實證結果發現，情緒指數與人工智慧概念股股價走勢有其影響，推測散戶看到新聞報導後會受到新聞文本的影響進而影響股價走勢，並於2~3 天影響最為明顯。
另外，本研究實驗結果中也發現結合情緒指數與技術指標建立的分類模型優於單純以技術指標分類模型來預測股價的漲跌趨勢。而加入其他間接指標如國際指標、總體經濟指標、台股資訊指標建立的分類模型優於結合情緒指數與技術指標建立的分類模型，整體分類準確度達82%。
在結合情緒指數與技術指標的分類模型上，以分類器的準確度及召回率效果而言，KNN的準確率為0.8 及召回率為0.67的分類結果較為優異，以F1-measure而言則是0.65 的隨機森林效果較為優異。		zh_TW
dc.description.tableofcontents 第一章 緒論 1
第一節 研究背景 1
第二節 研究動機與目的 10
第二章 文獻探討 14
第一節 股票預測的發展 14
第二節 臺灣股票市場 16
一、臺灣股票市場現況 16
二、臺灣股票市場之效率性相關研究 16
第三節 投資人情緒與報酬 19
第四節 新聞與股評對股價之關係 20
第五節 情感分析 21
一、情感分析的數據來源 22
二、情感分析的方法 23
第六節 主題模型 28
第七節 小結 29
第三章 研究方法 30
第一節 資料蒐集 31
第二節 文本預處理 31
第三節 文本主題分群 32
第四節 情緒傾向標注 32
第五節 時間序列與視覺化分析 35
第六節 建立分類模型 38
第七節 驗證分類模型 39
第四章 實驗結果 40
第一節 資料蒐集 40
第二節 文本主題分群 40
第三節 時間序列與視覺化分析 43
第四節 分類模型實驗結果 79
第五章 研究結論與未來建議 83
第一節 研究結論 83
第二節 未來建議 86
參考文獻 87
附表 95
I. 情緒指數與人工智慧概念股股價指數 95
II. 情緒指標與國際指標趨勢線分析圖 106
III. 情緒指標與總體經濟指標趨勢線分析圖 112
IV. 情緒指數與技術指標趨勢線分析圖 120		zh_TW
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G1053560163en_US
dc.subject (關鍵詞) 情感分析zh_TW
dc.subject (關鍵詞) 文字探勘zh_TW
dc.subject (關鍵詞) 機器學習zh_TW
dc.subject (關鍵詞) 時間序列分析zh_TW
dc.subject (關鍵詞) 人工智慧概念股zh_TW
dc.title (題名) 應用情感分析於股票趨勢預測 -以台灣人工智慧(AI)概念股為例zh_TW
dc.title (題名) Sentiment Analysis in Taiwan Artificial Intelligence Concept Stocken_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) [ 1 ] 王宇辰. (2011).亞洲國家股市弱式效率之實證研究.
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dc.identifier.doi (DOI) 10.6814/THE.NCCU.MIS.003.2018.A05-