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題名 各類新聞與正負面情緒對房市之影響：文字探勘之應用
Application of Text Mining: The Influence of Media Sentiment on Real Estate Market By Different News Topics and Positive/Negative Sentiment
作者 郭偉傑
Kuo, Wei-Chieh
貢獻者 陳明吉
郭偉傑
Kuo, Wei-Chieh
關鍵詞 文字探勘
情緒分析
房地產市場
Text Mining
Sentiment Analysis
Real Estate Market
日期 2020
上傳時間 3-Aug-2020 17:34:36 (UTC+8)
摘要 　　本研究透過於聯合知識庫蒐集2009年至2018年有關房市、股市、勞動市場與人口統計新聞共計70,533篇，並應用文字探勘與情緒分析技術，利用財金領域辭典作為分析情感的依據，計算各個不同新聞主題每月所隱含的情緒指標，來研究房市參與者會受到哪些主題的新聞所影響進而做出相對應的房市交易決策行為改變房價、房屋交易量、房屋流通天數與房屋議價空間。另外為了分析房市交易資訊是否具有正負面影響力不同的情形，本研究在計算情緒指標上也額外分別建立了正面與負面的情緒指標，來探討房市參與者較容易受到正面亦或是負面情緒所影響；此外，本研究為了探討媒體情緒與房市交易資訊之因果關係，亦採用Granger因果關係檢定來進行驗證。

　　本研究發現，房市媒體情緒將能顯著影響下一期的房價、房屋交易量、流通天數與議價空間，而將房市主題拆分為更細的主題後，也發現如租屋、房屋供給、房市政策與房市信用狀況新聞媒體情緒皆能顯著影響下一期的房價。除了房市以外的市場中，本研究也發現股市、勞動市場、人口統計媒體情緒也會顯著影響下一期房價，是故本研究證實了不只房市新聞媒體情緒將會顯著影響下一期房市交易資訊，若將房市媒體情緒做更細緻的拆分或是納入不同市場的媒體情緒，也能對未來房市交易資訊具有顯著的影響。

　　正負面媒體情緒中，本研究發現許多不同主題新聞中正面情緒與負面情緒影響力不同之現象；因果關係驗證上，本研究發現房市媒體情緒波動會造成房價、房屋交易量、房屋流通天數與房屋議價空間之改變，具有顯著因果關係。
Base on the vigorous development of text mining and sentiment analysis in recent years, it has also been gradually applied in various financial markets. This research collect news about the housing market, stock market, labor market and demographics from 2009 to 2018 via Udndata.com and capture 70,533 articles. Through text mining and sentiment analysis techniques, we constructed a series of monthly sentiment for every news topic and examine the relationship between the media sentiment and the housing market. Besides, we also separately established positive and negative sentiment index to explore whether housing market participants are more susceptible to positive or negative sentiment. In the end, we also used causality test to check the relation between sentiment and the houseing market.

The empirical results shows that the housing market sentiment will significantly affect the trading volume and the wiggle room in the next period. Also, after splitting the housing market media sentiment into more detailed themes, it also found that such as rental, housing supply, housing market policy and the credit situation media sentiment can significantly affect the house prices. In markets other than the housing market, we also found that stock market, labor market, demographic media sentiment will also significantly affect the house prices. To conclude this study, we confirmed that not only the housing market news media sentiment but also stock market, labor market and demographics media sentiment significantly affect the housing market. Besides, we found the Positive/Negative sentiment influence the housing market differently. In the end, we also found house media sentiment would Granger cause the housing market.
參考文獻 英文參考文獻
Baker, M., Wurgler, J., (2006), Investor sentiment and the cross‐section of stock returns, Journal of Finance, 61, 1645–1680.
Ball-Rokeach, S., DeFleur, M. L., (1976), A Dependency Model of Mass Media Effects. Communication Research, 3(1), 3-21.
Barber, B., & 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.
Baumeister, R., Bratslavsky, E., Finkenauer, C., Vohs, K. D., (2001), Bad is stronger than good. Review of General Psychology, 5, 323–370.
Beracha, E., Wintoki, B., (2013), Forecasting residential real estate price changes from online search activity, Journal of Real Estate Research, 35, 283-312.
Boiy, E., Moens, M. F., (2009), A machine learning approach to sentiment analysis in multilingual web texts, Information Retrieval, 12, 526-558.
Calomiris, C. W., & Mamaysky, H., (2019), How news and its context drive risk and returns around the world. Journal of Financial Economics, 133, 299–336.
Chau, F., Deesomark, R.,& Koutmos, D., (2016), Does investor sentiment really matter? International Review of Financial Analysis, 48, 221-232.
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.
Dong, Z. D., Dong, Q., Hao, C., (2010), HowNet and its computation of meaning, In Proceedings of the 23rd International Conference on Computational Linguistics: Demonstrations, Beijing, China.
Dougal, C., Engelberg, J., García, D.,& Parsons, C., (2012), Journalists and the Stock Market. The Review of Financial Studies, 25(3), 639-679.
Dumas, B., Kurshev, A.,& Uppal, R., (2009), Equilibrium portfolio strategies in the presence of sentiment risk and excess volatility, Journal of Finance, 64, 579-629.
Fayyad, U., Piatetsky-Shapiro, G.,& Smyth, P., (1996), From Data Mining to Knowledge Discovery: An Overview, Advances in Knowledge Discovery and Data Mining, 495-515.
French, K., Schwert, W.,& Stambaugh, R., (1987), Expected stock returns and volatility, Journal of Financial Economics, 19, 3-29.
Gandomi, A., Haider, M., (2015), Beyond the hype: Big data concepts, methods, and analytics, International Journal of Information Management, 35(2), 137-144.
Godbole, N., Srinivasaiah, M., & Skiena, S., (2007), Large-scale sentiment analysis for news and blogs, In Proceedings of the International Conference on Weblogs and Social Media (ICWSM), Boulder, CO, USA
Hu, M., Liu, B., (2004), Mining opinion features in customer reviews, In Proceedings of AAAI, 755-760.
Kahneman, D., & Tversky, A., (1979), Prospect Theory: An Analysis of Decision Under Risk, Econometrica, 47, 263–291.
Keshk, W., Wang, J., (2018), Determinants of the relationship between investor sentiment and analysts’ private information production, Journal of Business Finance & Accounting, 45, 9-10.
Keynes, J. M., (1936), The General Theory of Employment, Interest and Money, London: Harcourt Brace Jovanovich
Ku, L. W., Chen, H. H., (2007), Mining opinions from the web: beyond relevance retrieval, Mining Web Resources for Enhancing Information Retrieval, 58, 1838-1850.
Ku, L. W., Lo, Y. S., & Chen, H. H., (2007), Using polarity scores of words for sentence-level opinion extraction, In Proceedings of NTCIR-6 workshop meeting, Tokyo, Japan.
Lai, R. N., & Order, R. V., (2010), Momentum and House Price Growth in the United States: Anatomy of a Bubble, Real Estate Economics, 38(4), 753-773.
Liu, B., (2012), Sentiment Analysis and Opinion Mining, Morgan & Claypool Publishers
Loughran, T., Mcdonald, B., (2011), When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10‐Ks, The Journal of Finance, 66(1), 35-65.
Maynard, D., Funk, A., (2011), Automatic detection of political opinions in tweets, In Proceedings of the 8th international conference on the semantic web, ESWC, 11, 88-99.
McQuail, D., (1977), The influence and effects of mass media, Mass Communication and Society, London: Edward Arnold Ltd, 70-94.
Merton, R., (1973), A Rational Theory of Option Pricing, Bell Journal of Economics and Management Science, 4(1), 141-183
Nelson, D. B., (1991), Conditional heteroskedasticity in asset returns: A new approach, Econometrica, 59, 347-370.
Peng, L., Xiong, W., (2006), Investor attention, overconfidence and category learning, Journal of Financial Economics, 80, 563-602.
Rozin, P., Royzman, E. B., (2001), Negativity bias, negativity dominance, and contagion, Personality and Social Psychology Review, 5, 296-320.
Saydometov, S., Sabherwala, S., Aroul, R. R., (2018), Sentiment and Housing Returns, Dallas Baptist University, working paper
Shiller, R. J., (2000), Irrational Exuberance, Philosophy and Public Policy Quarterly, 20(1), 18-23.
Shiller, R. J., (2005), Irrational Exuberance, Princeton: NJ: Princeton University Press.
Solomon, D. H., (2012), Selective publicity and stock prices, Journal of Finance, 67, 599-637.
Soo, C. K., (2018), Quantifying Sentiment with News Media across Local Housing Markets, The Review of Financial Studies, 31(10), 3689-3719.
Sullivan, D. (2001), Document Warehousing and Text Mining: Techniques for Improving Business Operations, Marketing, and Sales, John Wiley & Sons, Inc., New York, NY, USA
Tetlock, P. C., (2007), Giving Content to Investor Sentiment: The Role of Media in the Stock Market, The Journal of Finance, 62(3), 1139-1168.
Tetlock, P. C., (2008), More Than Words: Quantifying Language to Measure Firms` Fundamentals, The Journal of Finance, 63(3), 1437-1467.
Tetlock, P. C., Saar-Tsechansky, M., Macskassy, S., (2008), More Than Words: Quantifying Language to Measure Firms` Fundamentals, The Journal of Finance, 63(3), 1437-1467.
Walker, C., (2014), Housing booms and media coverage. Applied Economics, 46(32), 3954-3967.
Wu, C. H., Lin C. J., (2017), The impact of media coverage on investor trading behavior and stock returns, Pacific‐Basin Finance Journal, 43, 151-172.
Zakoian, J. M., (1994), Threshold heteroskedasticity models. Journal of Economic Dynamics and Control, 15, 931-955.

中文參考文獻
呂旻哲, (2018), 房價供需層面變數與信義房價指數、國泰房地產指數及房價綜合趨勢分數之分析, 中華大學資訊管理學研究所
李慶堂, (2014), Text Mining技術淺談, 國立台灣大學計算機及資訊網路中心電子報,31.
林宜萱, (2013), 財經領域情緒辭典之建置與其有效性之驗證-以財經新聞為元件, 臺灣大學會計學研究所
朱芳妮, 楊茜文, 黃御維, & 陳明吉, (2020), 媒體傳播效應與房市變化關聯性之驗證, 管理學報, forthcoming
彭建文, & 張金鶚, (2000), 總體經濟對房地產景氣影響之研究. 國家科學委員會研究彙刊:人文及社會科學, 10(3), 330-343.
蔡怡純, & 陳明吉, (2013), 房價之不對稱均衡調整：門檻誤差修正模型應用. 臺灣土地研究, 16(1), 37-58.
描述 碩士
國立政治大學
財務管理學系
107357030
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0107357030
資料類型 thesis
dc.contributor.advisor 陳明吉zh_TW
dc.contributor.author (Authors) 郭偉傑zh_TW
dc.contributor.author (Authors) Kuo, Wei-Chiehen_US
dc.creator (作者) 郭偉傑zh_TW
dc.creator (作者) Kuo, Wei-Chiehen_US
dc.date (日期) 2020en_US
dc.date.accessioned 3-Aug-2020 17:34:36 (UTC+8)-
dc.date.available 3-Aug-2020 17:34:36 (UTC+8)-
dc.date.issued (上傳時間) 3-Aug-2020 17:34:36 (UTC+8)-
dc.identifier (Other Identifiers) G0107357030en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/130972-
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) 107357030zh_TW
dc.description.abstract (摘要) 　　本研究透過於聯合知識庫蒐集2009年至2018年有關房市、股市、勞動市場與人口統計新聞共計70,533篇，並應用文字探勘與情緒分析技術，利用財金領域辭典作為分析情感的依據，計算各個不同新聞主題每月所隱含的情緒指標，來研究房市參與者會受到哪些主題的新聞所影響進而做出相對應的房市交易決策行為改變房價、房屋交易量、房屋流通天數與房屋議價空間。另外為了分析房市交易資訊是否具有正負面影響力不同的情形，本研究在計算情緒指標上也額外分別建立了正面與負面的情緒指標，來探討房市參與者較容易受到正面亦或是負面情緒所影響；此外，本研究為了探討媒體情緒與房市交易資訊之因果關係，亦採用Granger因果關係檢定來進行驗證。

　　本研究發現，房市媒體情緒將能顯著影響下一期的房價、房屋交易量、流通天數與議價空間，而將房市主題拆分為更細的主題後，也發現如租屋、房屋供給、房市政策與房市信用狀況新聞媒體情緒皆能顯著影響下一期的房價。除了房市以外的市場中，本研究也發現股市、勞動市場、人口統計媒體情緒也會顯著影響下一期房價，是故本研究證實了不只房市新聞媒體情緒將會顯著影響下一期房市交易資訊，若將房市媒體情緒做更細緻的拆分或是納入不同市場的媒體情緒，也能對未來房市交易資訊具有顯著的影響。

　　正負面媒體情緒中，本研究發現許多不同主題新聞中正面情緒與負面情緒影響力不同之現象；因果關係驗證上，本研究發現房市媒體情緒波動會造成房價、房屋交易量、房屋流通天數與房屋議價空間之改變，具有顯著因果關係。		zh_TW
dc.description.abstract (摘要) Base on the vigorous development of text mining and sentiment analysis in recent years, it has also been gradually applied in various financial markets. This research collect news about the housing market, stock market, labor market and demographics from 2009 to 2018 via Udndata.com and capture 70,533 articles. Through text mining and sentiment analysis techniques, we constructed a series of monthly sentiment for every news topic and examine the relationship between the media sentiment and the housing market. Besides, we also separately established positive and negative sentiment index to explore whether housing market participants are more susceptible to positive or negative sentiment. In the end, we also used causality test to check the relation between sentiment and the houseing market.

The empirical results shows that the housing market sentiment will significantly affect the trading volume and the wiggle room in the next period. Also, after splitting the housing market media sentiment into more detailed themes, it also found that such as rental, housing supply, housing market policy and the credit situation media sentiment can significantly affect the house prices. In markets other than the housing market, we also found that stock market, labor market, demographic media sentiment will also significantly affect the house prices. To conclude this study, we confirmed that not only the housing market news media sentiment but also stock market, labor market and demographics media sentiment significantly affect the housing market. Besides, we found the Positive/Negative sentiment influence the housing market differently. In the end, we also found house media sentiment would Granger cause the housing market.		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 5
第三節 研究流程 6
第二章 文獻回顧 7
第一節 新聞媒體對資產市場的影響 7
第二節 投資人情緒對資產市場的影響 10
第三節 文字探勘技術與情緒分析 14
第三章 研究設計 16
第一節 研究方法架構 16
第二節 文字探勘與情緒分析流程 18
第三節 研究模型 22
第四節 新聞資料來源 24
第五節 其他變數定義及衡量 25
第六節 實證流程 29
第四章 實證分析 30
第一節 樣本資料分析 30
第二節 相關性分析 33
第三節 不同主題新聞媒體情緒對房市之影響 41
第四節 不同主題新聞正負面媒體情緒對房市的影響 46
第五節 房市媒體情緒之因果關係 52
第五章 結論與建議 57
第一節 結論 57
第二節 建議與限制 59
參考文獻 60
附錄一：蒐集不同主題新聞所用之關鍵字 64
附錄二：共整合檢定 65		zh_TW
dc.format.extent 3151053 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0107357030en_US
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) 房地產市場zh_TW
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.title (題名) 各類新聞與正負面情緒對房市之影響：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 By Different News Topics and Positive/Negative Sentimenten_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) 英文參考文獻
Baker, M., Wurgler, J., (2006), Investor sentiment and the cross‐section of stock returns, Journal of Finance, 61, 1645–1680.
Ball-Rokeach, S., DeFleur, M. L., (1976), A Dependency Model of Mass Media Effects. Communication Research, 3(1), 3-21.
Barber, B., & 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.
Baumeister, R., Bratslavsky, E., Finkenauer, C., Vohs, K. D., (2001), Bad is stronger than good. Review of General Psychology, 5, 323–370.
Beracha, E., Wintoki, B., (2013), Forecasting residential real estate price changes from online search activity, Journal of Real Estate Research, 35, 283-312.
Boiy, E., Moens, M. F., (2009), A machine learning approach to sentiment analysis in multilingual web texts, Information Retrieval, 12, 526-558.
Calomiris, C. W., & Mamaysky, H., (2019), How news and its context drive risk and returns around the world. Journal of Financial Economics, 133, 299–336.
Chau, F., Deesomark, R.,& Koutmos, D., (2016), Does investor sentiment really matter? International Review of Financial Analysis, 48, 221-232.
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.
Dong, Z. D., Dong, Q., Hao, C., (2010), HowNet and its computation of meaning, In Proceedings of the 23rd International Conference on Computational Linguistics: Demonstrations, Beijing, China.
Dougal, C., Engelberg, J., García, D.,& Parsons, C., (2012), Journalists and the Stock Market. The Review of Financial Studies, 25(3), 639-679.
Dumas, B., Kurshev, A.,& Uppal, R., (2009), Equilibrium portfolio strategies in the presence of sentiment risk and excess volatility, Journal of Finance, 64, 579-629.
Fayyad, U., Piatetsky-Shapiro, G.,& Smyth, P., (1996), From Data Mining to Knowledge Discovery: An Overview, Advances in Knowledge Discovery and Data Mining, 495-515.
French, K., Schwert, W.,& Stambaugh, R., (1987), Expected stock returns and volatility, Journal of Financial Economics, 19, 3-29.
Gandomi, A., Haider, M., (2015), Beyond the hype: Big data concepts, methods, and analytics, International Journal of Information Management, 35(2), 137-144.
Godbole, N., Srinivasaiah, M., & Skiena, S., (2007), Large-scale sentiment analysis for news and blogs, In Proceedings of the International Conference on Weblogs and Social Media (ICWSM), Boulder, CO, USA
Hu, M., Liu, B., (2004), Mining opinion features in customer reviews, In Proceedings of AAAI, 755-760.
Kahneman, D., & Tversky, A., (1979), Prospect Theory: An Analysis of Decision Under Risk, Econometrica, 47, 263–291.
Keshk, W., Wang, J., (2018), Determinants of the relationship between investor sentiment and analysts’ private information production, Journal of Business Finance & Accounting, 45, 9-10.
Keynes, J. M., (1936), The General Theory of Employment, Interest and Money, London: Harcourt Brace Jovanovich
Ku, L. W., Chen, H. H., (2007), Mining opinions from the web: beyond relevance retrieval, Mining Web Resources for Enhancing Information Retrieval, 58, 1838-1850.
Ku, L. W., Lo, Y. S., & Chen, H. H., (2007), Using polarity scores of words for sentence-level opinion extraction, In Proceedings of NTCIR-6 workshop meeting, Tokyo, Japan.
Lai, R. N., & Order, R. V., (2010), Momentum and House Price Growth in the United States: Anatomy of a Bubble, Real Estate Economics, 38(4), 753-773.
Liu, B., (2012), Sentiment Analysis and Opinion Mining, Morgan & Claypool Publishers
Loughran, T., Mcdonald, B., (2011), When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10‐Ks, The Journal of Finance, 66(1), 35-65.
Maynard, D., Funk, A., (2011), Automatic detection of political opinions in tweets, In Proceedings of the 8th international conference on the semantic web, ESWC, 11, 88-99.
McQuail, D., (1977), The influence and effects of mass media, Mass Communication and Society, London: Edward Arnold Ltd, 70-94.
Merton, R., (1973), A Rational Theory of Option Pricing, Bell Journal of Economics and Management Science, 4(1), 141-183
Nelson, D. B., (1991), Conditional heteroskedasticity in asset returns: A new approach, Econometrica, 59, 347-370.
Peng, L., Xiong, W., (2006), Investor attention, overconfidence and category learning, Journal of Financial Economics, 80, 563-602.
Rozin, P., Royzman, E. B., (2001), Negativity bias, negativity dominance, and contagion, Personality and Social Psychology Review, 5, 296-320.
Saydometov, S., Sabherwala, S., Aroul, R. R., (2018), Sentiment and Housing Returns, Dallas Baptist University, working paper
Shiller, R. J., (2000), Irrational Exuberance, Philosophy and Public Policy Quarterly, 20(1), 18-23.
Shiller, R. J., (2005), Irrational Exuberance, Princeton: NJ: Princeton University Press.
Solomon, D. H., (2012), Selective publicity and stock prices, Journal of Finance, 67, 599-637.
Soo, C. K., (2018), Quantifying Sentiment with News Media across Local Housing Markets, The Review of Financial Studies, 31(10), 3689-3719.
Sullivan, D. (2001), Document Warehousing and Text Mining: Techniques for Improving Business Operations, Marketing, and Sales, John Wiley & Sons, Inc., New York, NY, USA
Tetlock, P. C., (2007), Giving Content to Investor Sentiment: The Role of Media in the Stock Market, The Journal of Finance, 62(3), 1139-1168.
Tetlock, P. C., (2008), More Than Words: Quantifying Language to Measure Firms` Fundamentals, The Journal of Finance, 63(3), 1437-1467.
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dc.identifier.doi (DOI) 10.6814/NCCU202000812en_US