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題名 媒體情緒對大台北房市之影響: 文字探勘之應用
Application of Text Mining: The Influence of Media Sentiment on Real Estate Market in Taipei Metropolitan Area
作者 黃御維
Huang, Yu-Wei
貢獻者 陳明吉
Chen, Ming-Chi
黃御維
Huang, Yu-Wei
關鍵詞 網路爬蟲
文字探勘
情緒分析
媒體情緒指數
房地產市場
Web crawler
Text mining
Sentiment analysis
Real estate market
Media sentiment index
日期 2019
上傳時間 7-Aug-2019 16:04:14 (UTC+8)
摘要 房地產市場的產品異質性高，再加上台灣房地產市場的資訊不對稱的問題嚴重，往往新聞媒體的資訊與消息成為市場參與者分析房市之重要來源，導致市場參與者較容易地受到媒體的風向影響，改變其對於房市的觀點。本研究透過網路爬蟲抓取2006年至2017年間共21,678篇有關台北市與新北市的房市與總體經濟新聞作為研究資料，透過文字探勘中的情緒分析方式，探討媒體情緒指數與房地產市場之關係，選取房價、房屋交易量、房屋流通天數與議價空間為房市狀況指標。本研究發現，不論新北市或是台北市，本研究編制的媒體情緒指數對於其房價、交易量與流通天數都是呈現顯著的影響，表示媒體對於房市的報導態度，會直接或間接地影響市場參與者之想法或預期，進而投入房地產市場，此外房市新聞報導的頻率對於房價、成交量與流通天數也有顯著的相關性，亦表示新聞報導量的增加，將會推升市場參與者對於下一期房市之預期。本研究也透過Copula動態相關分析，發現兩地區房價和交易量與其媒體情緒指數之動態相關性約在2012年時開始產生明顯變化，甚至由正相關轉為負相關，本研究認為此相關性具有明顯的變化是因為當時政府積極推動各項房市政策以抑制房價，例如:2011年奢侈稅的上路， 2012年實施豪宅限貸令與實施時價登錄，因此房市政策的實施，也會影響市場參與者的態度與房市展望。
The real estate market exist high product heterogeneity, and there also is a serious problem of information asymmetry in the Taiwan real estate market. The information and news from news media often become an important source for market participants to analyze the housing market, which makes it easier for market participants to be influenced by the media`s spin control and change their perspective on the housing market. We used web crawler to download 21,678 articles about the housing market and macroeconomics news of Taipei City and New Taipei City from 2006 to 2017. Through the method of text mining and emotional analysis, we want to explore the relationship between the media sentiment index and the real estate market, including house price, trading volume, circulation days and bargaining space. We found that regardless of New Taipei City or Taipei City, the media sentiment index of the two regions has a significant impact on their housing prices, trading volume and circulation days, indicating that the attitude of media`s reporting towards the housing market would directly or indirectly affect the ideas or expectations of market participants, and then join the real estate market. In addition, the frequency of news reporting has a significant correlation with the price, volume and circulation days. It also indicates that the increase in volume of news will boost market participants` expectations for the housing market performance in next period.We also use Copula dynamic correlation analysis and found that the dynamic correlation between house prices and media sentiment index in the two regions began to change significantly in 2012, even from positive correlation to negative correlation. We believe that this correlation has obvious changes because the government actively promoted various housing policies to curb housing prices.
參考文獻 英文參考文獻

Anne, K., & Poteet, S. R. (2007). Natural Language
Processing and Text Mining: Springer.
Baker, M., & Wurgler, J. (2007). Investor Sentiment in the
Stock Market. Journal of Economic Perspectives, 21(2),
129-151.
Ball-Rokeach, Sandra J., & DeFleur, M. (1976). A Dependency
Model of Mass Media Effects. Communication Research,
3(1), 3-21
Barber, B. M., & Odean, T. (2008). All That Glitters: The
Effect of Attention and News on the Buying Behavior of
Individual and Institutional Investors. The Review of
Financial Studies, 21(2), 785-818.
Beracha, E., & Wintoki, M. B. (2013). Forecasting
Residential Real Estate Price Changes from Online
Search Activity. Journal of Real Estate Research,
35(3), 283-312.
Chen, M.-C., & Patel, K. (2002). An empirical analysis of
determination of house prices in the Taipei area.
Taiwan Economic Review, 30(4), 563-595.
Chen, M.-C., Tsai, I.-C., & Chang, C.-O. (2007). House
prices and household income: Do they move apart?
Evidence from Taiwan. Habitat International, 31(2),
243-256.
Dong, Z., Dong, Q., & Hao, C. (2010). HowNet and its
computation of meaning. Paper presented at the
Proceedings of the 23rd International Conference on
Computational Linguistics: Demonstrations, Beijing,
China.
Dougal, C., Engelberg, J., García, D., & Parsons, C. A.
(2012). Journalists and the Stock Market. The Review of
Financial Studies, 25(3), 639-679.
Engelberg, J. E., & Parsons, C. A. (2011). The Causal
Impact of Media in Financial Markets. The Journal of
Finance, 66(1), 67-97.
Feldman, R., & Sanger, J. (2002). The Text Mining Handbook:
Cambridge University Press.
Fenzl, T., & Pelzmann, L. (2012). Psychological and Social
Forces Behind Aggregate Financial Market Behavior.
Journal of Behavioral Finance, 13(1), 56-65.
Garcia, D. (2013). Sentiment during Recessions. The Journal
of Finance, 68(3), 1267-1300.
Gentzkow, M., & Shapiro, J. M. (2010). What Drives Media
Slant? Evidence From U.S. Daily Newspapers. Journal of
the econometric society, 78(1), 35-71.
Godbole, N., Srinivasaiah, M., & Skiena, S. (2007). Large-
Scale Sentiment Analysis for News and Blogs. ICWSM,
7(21), 219-222.
Granziera, E., & Kozicki, S. (2015). House price dynamics:
Fundamentals and expectations. Journal of Economic
Dynamics and Control, 60, 152-165.
Hanley, K. W., & Hoberg, G. (2010). The Information Content
of IPO Prospectuses. The Review of Financial Studies,
23(7), 2821-2864.
Hong, H., & Stein, J. C. (2007). Disagreement and the Stock
Market. Journal of Economic Perspectives, 21(2), 109-
128.
Hu, M., & Liu, B. (2004). Mining Opinion Features in
Customer Reviews Proceedings of the 19th National
Conference on Artificial Intelligence, 755-776.
Hui, E. C. M., Dong, Z., Jia, S., & Lam, C. H. L. (2017).
How does sentiment affect returns of urban housing?
Habitat International, 64, 71-84.
Keynes, J. M. (1936). The General Theory of Employment,
Interest and Money. Palgrave Macmillan.
Ku, L.-W., Lo, Y.-S., & Chen, H.-H. (2007). Using polarity
scores of words for sentence-level opinion extraction.
Paper presented at the Proceedings of NTCIR-6 workshop
meeting, Tokyo, Japan.
Lai, R. N., & Order, R. A. V. (2010). Momentum and House
Price Growth in the United States: Anatomy of a Bubble.
Real Estate Economics, 38(4), 753-773.
Lambertini, L., Mendicino, C., & Punzi, M. T. (2013).
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林秋瑾, 王健安, & 張金鶚. (1997). 房地產景氣與總體經濟景氣於時
間上領先、同時、落後關係之探討. 國家科學委員會彙刊；人文及社
會科學, 7(1), 35-56.
張津挺. (2015). 中文財務情緒字典建構與其在財務新聞分析之應用.
(碩士), 臺北市立大學資訊科學系.
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科學委員會研究彙刊:人文及社會科學, 10(3), 330-343.
廖慧玲. (2011). 貨幣供給、新台幣匯率對房價指數與股價報酬率關聯性
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蔡怡純, & 陳明吉. (2013). 房價之不對稱均衡調整：門檻誤差修正模
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鍾任明, 李維平, & 吳澤民. (2007). 運用文字探勘於日內股價漲跌趨
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描述 碩士
國立政治大學
財務管理學系
106357018
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0106357018
資料類型 thesis
dc.contributor.advisor 陳明吉zh_TW
dc.contributor.advisor Chen, Ming-Chien_US
dc.contributor.author (Authors) 黃御維zh_TW
dc.contributor.author (Authors) Huang, Yu-Weien_US
dc.creator (作者) 黃御維zh_TW
dc.creator (作者) Huang, Yu-Weien_US
dc.date (日期) 2019en_US
dc.date.accessioned 7-Aug-2019 16:04:14 (UTC+8)-
dc.date.available 7-Aug-2019 16:04:14 (UTC+8)-
dc.date.issued (上傳時間) 7-Aug-2019 16:04:14 (UTC+8)-
dc.identifier (Other Identifiers) G0106357018en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/124696-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 財務管理學系zh_TW
dc.description (描述) 106357018zh_TW
dc.description.abstract (摘要) 房地產市場的產品異質性高，再加上台灣房地產市場的資訊不對稱的問題嚴重，往往新聞媒體的資訊與消息成為市場參與者分析房市之重要來源，導致市場參與者較容易地受到媒體的風向影響，改變其對於房市的觀點。本研究透過網路爬蟲抓取2006年至2017年間共21,678篇有關台北市與新北市的房市與總體經濟新聞作為研究資料，透過文字探勘中的情緒分析方式，探討媒體情緒指數與房地產市場之關係，選取房價、房屋交易量、房屋流通天數與議價空間為房市狀況指標。本研究發現，不論新北市或是台北市，本研究編制的媒體情緒指數對於其房價、交易量與流通天數都是呈現顯著的影響，表示媒體對於房市的報導態度，會直接或間接地影響市場參與者之想法或預期，進而投入房地產市場，此外房市新聞報導的頻率對於房價、成交量與流通天數也有顯著的相關性，亦表示新聞報導量的增加，將會推升市場參與者對於下一期房市之預期。本研究也透過Copula動態相關分析，發現兩地區房價和交易量與其媒體情緒指數之動態相關性約在2012年時開始產生明顯變化，甚至由正相關轉為負相關，本研究認為此相關性具有明顯的變化是因為當時政府積極推動各項房市政策以抑制房價，例如:2011年奢侈稅的上路， 2012年實施豪宅限貸令與實施時價登錄，因此房市政策的實施，也會影響市場參與者的態度與房市展望。zh_TW
dc.description.abstract (摘要) The real estate market exist high product heterogeneity, and there also is a serious problem of information asymmetry in the Taiwan real estate market. The information and news from news media often become an important source for market participants to analyze the housing market, which makes it easier for market participants to be influenced by the media`s spin control and change their perspective on the housing market. We used web crawler to download 21,678 articles about the housing market and macroeconomics news of Taipei City and New Taipei City from 2006 to 2017. Through the method of text mining and emotional analysis, we want to explore the relationship between the media sentiment index and the real estate market, including house price, trading volume, circulation days and bargaining space. We found that regardless of New Taipei City or Taipei City, the media sentiment index of the two regions has a significant impact on their housing prices, trading volume and circulation days, indicating that the attitude of media`s reporting towards the housing market would directly or indirectly affect the ideas or expectations of market participants, and then join the real estate market. In addition, the frequency of news reporting has a significant correlation with the price, volume and circulation days. It also indicates that the increase in volume of news will boost market participants` expectations for the housing market performance in next period.We also use Copula dynamic correlation analysis and found that the dynamic correlation between house prices and media sentiment index in the two regions began to change significantly in 2012, even from positive correlation to negative correlation. We believe that this correlation has obvious changes because the government actively promoted various housing policies to curb housing prices.en_US
dc.description.tableofcontents 第一章 緒論 1
第一節 研究背景與動機 1
第二節 研究問題與目的 4
第三節 研究流程 5
第二章 文獻探討 6
第一節 新聞媒體對資產市場的影響 6
第二節 投資人情緒對資產市場的影響 8
第三節 文字探勘技術的應用與文獻 11
第四節 總體經濟對不動產之影響 16
第五節 小結 17
第三章 研究設計 18
第一節 研究方法架構 18
第二節 新聞資料來源 19
第三節 文字探勘流程 20
第四節 研究模型 26
第五節 變數定義及衡量方法 28
第六節 研究方法 33
第四章 實證分析 37
第一節 樣本資料分析 37
第二節 相關性分析 40
第三節 媒體情緒指數對房市之影響 47
第四節 媒體情緒指數之因果關係 53
第五節 媒體情緒指數之衝擊反應 57
第五章、結論與建議 60
第一節 結論 60
第二節 建議與限制 63
參考文獻 65
附錄 69		zh_TW
dc.format.extent 1705994 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0106357018en_US
dc.subject (關鍵詞) 網路爬蟲zh_TW
dc.subject (關鍵詞) 文字探勘zh_TW
dc.subject (關鍵詞) 情緒分析zh_TW
dc.subject (關鍵詞) 媒體情緒指數zh_TW
dc.subject (關鍵詞) 房地產市場zh_TW
dc.subject (關鍵詞) Web crawleren_US
dc.subject (關鍵詞) Text miningen_US
dc.subject (關鍵詞) Sentiment analysisen_US
dc.subject (關鍵詞) Real estate marketen_US
dc.subject (關鍵詞) Media sentiment indexen_US
dc.title (題名) 媒體情緒對大台北房市之影響: 文字探勘之應用zh_TW
dc.title (題名) Application of Text Mining: The Influence of Media Sentiment on Real Estate Market in Taipei Metropolitan Areaen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) 英文參考文獻

Anne, K., & Poteet, S. R. (2007). Natural Language
Processing and Text Mining: Springer.
Baker, M., & Wurgler, J. (2007). Investor Sentiment in the
Stock Market. Journal of Economic Perspectives, 21(2),
129-151.
Ball-Rokeach, Sandra J., & DeFleur, M. (1976). A Dependency
Model of Mass Media Effects. Communication Research,
3(1), 3-21
Barber, B. M., & Odean, T. (2008). All That Glitters: The
Effect of Attention and News on the Buying Behavior of
Individual and Institutional Investors. The Review of
Financial Studies, 21(2), 785-818.
Beracha, E., & Wintoki, M. B. (2013). Forecasting
Residential Real Estate Price Changes from Online
Search Activity. Journal of Real Estate Research,
35(3), 283-312.
Chen, M.-C., & Patel, K. (2002). An empirical analysis of
determination of house prices in the Taipei area.
Taiwan Economic Review, 30(4), 563-595.
Chen, M.-C., Tsai, I.-C., & Chang, C.-O. (2007). House
prices and household income: Do they move apart?
Evidence from Taiwan. Habitat International, 31(2),
243-256.
Dong, Z., Dong, Q., & Hao, C. (2010). HowNet and its
computation of meaning. Paper presented at the
Proceedings of the 23rd International Conference on
Computational Linguistics: Demonstrations, Beijing,
China.
Dougal, C., Engelberg, J., García, D., & Parsons, C. A.
(2012). Journalists and the Stock Market. The Review of
Financial Studies, 25(3), 639-679.
Engelberg, J. E., & Parsons, C. A. (2011). The Causal
Impact of Media in Financial Markets. The Journal of
Finance, 66(1), 67-97.
Feldman, R., & Sanger, J. (2002). The Text Mining Handbook:
Cambridge University Press.
Fenzl, T., & Pelzmann, L. (2012). Psychological and Social
Forces Behind Aggregate Financial Market Behavior.
Journal of Behavioral Finance, 13(1), 56-65.
Garcia, D. (2013). Sentiment during Recessions. The Journal
of Finance, 68(3), 1267-1300.
Gentzkow, M., & Shapiro, J. M. (2010). What Drives Media
Slant? Evidence From U.S. Daily Newspapers. Journal of
the econometric society, 78(1), 35-71.
Godbole, N., Srinivasaiah, M., & Skiena, S. (2007). Large-
Scale Sentiment Analysis for News and Blogs. ICWSM,
7(21), 219-222.
Granziera, E., & Kozicki, S. (2015). House price dynamics:
Fundamentals and expectations. Journal of Economic
Dynamics and Control, 60, 152-165.
Hanley, K. W., & Hoberg, G. (2010). The Information Content
of IPO Prospectuses. The Review of Financial Studies,
23(7), 2821-2864.
Hong, H., & Stein, J. C. (2007). Disagreement and the Stock
Market. Journal of Economic Perspectives, 21(2), 109-
128.
Hu, M., & Liu, B. (2004). Mining Opinion Features in
Customer Reviews Proceedings of the 19th National
Conference on Artificial Intelligence, 755-776.
Hui, E. C. M., Dong, Z., Jia, S., & Lam, C. H. L. (2017).
How does sentiment affect returns of urban housing?
Habitat International, 64, 71-84.
Keynes, J. M. (1936). The General Theory of Employment,
Interest and Money. Palgrave Macmillan.
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dc.identifier.doi (DOI) 10.6814/NCCU201900185en_US