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    政大典藏 > College of Commerce > Department of MIS > Theses >  Item 140.119/67096


    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/67096


    Title: 應用kNN文字探勘技術於分析新聞評論 影響股價漲跌趨勢之研究
    The Study of Analyzing Comments of News for Influence of Stock Price Trends Prediction by Using Knn Text Mining
    Authors: 詹智勝
    Chan, Chih Sheng
    Contributors: 楊建民
    Yang, Chien Ming
    詹智勝
    Chan, Chih Sheng
    Keywords: 網路口碑
    股價趨勢預測
    文字探勘
    kNN
    群集分析
    Internet Word-of-Mouth
    The Stock Trend Prediction
    Text Mining
    kNN
    Cluster Analysis
    Date: 2013
    Issue Date: 2014-07-01 12:06:17 (UTC+8)
    Abstract: 在網際網路快速發展下，大量使用者在獲取知識與新聞的管道，已由傳統媒體轉移到網路上。網路活動下使用者互動後所留下的訊息，也就是網路口碑，也逐漸受到重視。而隨著經濟發展，國人在固定薪資下無法負擔高房價、高物價的生活，如何透過投資理財來增加自身財富，已是非常普遍，其中又以股市投資為大眾所重視之途徑。

    網路新聞的發布，除了具有網路的即時性外，配合使用者閱讀內化後所留下的評論，應含有比網路新聞本身內容更多的資訊，投資者便可藉此找尋隱含之中大量市場消息與資訊。

    本研究為了在龐大的資料量中，幫助使用者挖掘其背後之涵義，進而提供投資預測，將蒐集網路新聞及其閱讀者評論共1068篇，並分為訓練資料與測試資料，使用文字探勘及相關技術做前處理，再透過kNN分群技術，計算訓練資料文件間相似度，將大量未知資料依其相似度做分群後，利用歷史股價訊息對群集結果之特徵分析解釋之並建立預測模型，最後透過測試資料將模型分群結果進行評估，進而對股價趨勢做出預測。
    With the rapid development of the Internet, the way of user access to knowledge and news transfer from traditional media to the network. Internet word-of-mouth, the message generated from users` interaction on internet, attracts more and more people`s attention. With economic development, people in the fixed salary cannot afford high prices and high price in live. People increase their own wealth through investment is very common, among which the stock market is the way to public attention.

    Internet news has the immediacy of the Internet. And the comments left with the user to read the internalization should contain more information than the Internet news. Investors can find the market news and information by Internet news and comments.

    In this study, in order to help the user to find the meaning behind the huge amount of data, and thus provide investment forecast. We will collect 1068 of internet news and reader reviews to divide into training data and test data using text mining and related technologies to do the pre-treatment, and then calculate the similarity between the training data by kNN, a lot of unknown data according to their similarity clustering. Cluster through the historical share price analysis and modeling. Finally, the model clustering results were evaluated through the test data to predict price trends. The prediction model from training data clustering, use test data to do the evaluation found: k = 15, the similarity threshold value = 0.05, cluster the results of the F-measure performance up to 56% rise in the cluster. K values and the similarity threshold will be adjusted to obtain the most favorable results of the model
    Reference: 一、 中文部分
    1. 喻欣凱，2008，運用支援向量機與文字探勘於股價漲跌趨勢之預測，輔仁大學資訊管理學系碩士論文。
    2. 陳均碩，2000，農業電子報使用者動機、行為與滿足程度之研究-以資策會「臺灣農業資訊網（TAIS）電子報」為例，國立臺灣大學農業推廣學研究所碩士論文。
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    6. 吳漢瑞，2011，應用文字探勘技術於臺灣上市公司重大訊息對股價影響之研究，國立政治大學資訊管理研究所碩士論文。
    7. 陳柏均，2011，文件距離為基礎kNN分群技術與新聞事件偵測追蹤之研究，國立政治大學資訊管理研究所碩士論文。
    8. 費翠，網路市場行家理論驗證與延伸---其網路資訊搜尋、口碑傳播、線上購物行為及個人特質研究，國立政治大學廣告研究所，2001。
     
    二、 英文部分
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    18. Murray, K. B. (1991). A test of services marketing theory: consumer information acquisition activities. The Journal of Marketing, 10-25.
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    三、 網路部分
    1. MM Days，http://mmdays.com/2007/05/16/knn/，2007/5/16。
    2. Pew Research Center，http://www.pewresearch.org/，2010。
    Description: 碩士
    國立政治大學
    資訊管理研究所
    100356044
    102
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0100356044
    Data Type: thesis
    Appears in Collections:[Department of MIS] Theses

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