Loading...
|
Please use this identifier to cite or link to this item:
https://nccur.lib.nccu.edu.tw/handle/140.119/76424
|
Title: | 應用探勘技術於社會輿情以預測捷運週邊房地產市場之研究 A Study of Applying Public Opinion Mining to Predict the Housing Market Near the Taipei MRT Stations |
Authors: | 吳佳芸 Wu, Chia Yun |
Contributors: | 楊建民 Yang, Jian Min 吳佳芸 Wu, Chia Yun |
Keywords: | 文字探勘 情緒探勘 房地產 移動平均 支援向量機 Text Mining Opinion Mining Housing Market Moving Average Support Vector Machine |
Date: | 2015 |
Issue Date: | 2015-07-13 11:07:28 (UTC+8) |
Abstract: | 因網際網路帶來的便利性與即時性,網路新聞成為社會大眾吸收與傳遞新聞資訊的重要管道之一,而累積的巨量新聞亦可反映出社會輿論對某特定新聞議題之即時反應、熱門程度以及情緒走向等。 因此,本研究期望借由意見探勘與情緒分析技術,從特定領域新聞中挖掘出有價值的關聯,並結合傳統機器學習建立一個房地產市場的預測模式,提供購屋決策的參考依據。 本研究搜集99年1月1日至103年6月30日共1,1150筆房地產新聞,以及8,165件捷運週邊250公尺內房屋買賣交易資料,運用意見探勘萃取意見詞彙進行情緒分析,並建立房市情緒與成交價量時間序列,透過半年移動平均、二次移動平均及成長斜率,瞭解社會輿情對房市行情抱持樂觀或悲觀,分析社會情緒與實際房地產成交間關聯性,以期能找出房地產買賣時機點,並進一步結合情緒及房地產的環境影響因素,藉由支援向量機建立站點房市的預測模型。 實證結果中,本研究發現房市情緒與成交價量之波動有一定的週期與相關性,且新捷運開通前一年將連帶影響整體捷運房市波動,當成交線穿越情緒線且斜率同時向上時,可做為適當的房市進場時機點。而本研究針對站點情緒與環境變數所建立之預測模型,其預測新捷運線站點之平均準確率為69.2%,而預測新捷運線熱門站點之準確率為78%,顯示模型於預測熱門站點上具有不錯的預測能力。 Nowadays, E-News have become an important way for people to get daily information. These enormous amounts of news could reflect public opinions on a particular attention or sentiment trends in news topics. Therefore, how to use opinion mining and sentiment analysis technology to dig out valuable information from particular news becomes the latest issue. In this study, we collected 1,1150 house news and 8,165 house transaction records around the MRT stations within 250 meters over the last five years. We extracted the emotion words from the news by manipulating opinion mining. Furthermore, we built moving average lines and the slope of the moving average in order to explore the relationship and entry point between public opinion and housing market. In conclusion, we indicated that there is a high correlation between the news sentiment and housing market. We also uses SVM algorithm to construct a model to predict housing hotspots. The results demonstrate that the SVM model reaches average accuracy at 69.2% and the model accuracy increases up to 78% for predicting housing hotspots. Besides, we also provide investors with a basis of entry point into the housing market by utilizing the moving average cross overs and slopes analysis and a better way of predicting housing hotspots. |
Reference: | 吳濟華、葉晉嘉、蔡源培, 2007, 台北捷運通車後車站周邊土地價格變動之實證分析, 土地經濟年刊,18,p1~26 李明翰, 2012, 以特徵價格法探討影響房價之因子-以新北市板橋區為例, 國立臺灣海洋大學應用經濟研究所碩士論文 李啟菁,2010, 中文部落格文章之意見分析, 國立台北科技大學資訊工程學系 林宇中,2003, 基於語意內容分析之情緒分類系統, 國立成功大學資訊工程研究所碩士論文 洪得洋、林祖嘉, 1999, 臺北市捷運系統與道路寬度對房屋價格影響之研究, 住宅學報 洪鴻達, 2010, 意見探勘在中文電影評論之應用, 國立交通大學管理學院資訊管理學程碩士論文 張冊蒼, 2012, 捷運通車對區域房價之影響-以蘆洲區為例, 國立中央大學產業經濟研究所在職專班碩士論文 張金鶚, 1993, 房地產真實交易易價格研究, 住宅宅學報,第一期,p.75-97 張金鶚, 花敬群, 彭建文, 楊宗憲, 2003, 房地產投資與市場分析理論與實務, 華泰書局 梁客川, 2003,不動產證券化之研究-我國「不動產證券化條例」草案為中心, 政大法研所碩士論文 連紹成, 2013, 捷運規劃及動工時期對房價的影響-以桃園地區透天住宅為例, 國立中央大學產業經濟研究碩士論文 陳少棠, 2012, 利用網路言論推測房地產交易溫度, 元智大學資訊管理學系碩士論文 彭建文、楊宗憲、楊詩韻, 2009, 捷運系統對不同區位房價影響分析─以營運階段為例, 運輸計劃季刊 彭宴玲,2005, 台北市綠地效益之評價-特徵價格法之應用,中國文化大學景觀學系碩士論文 曾琬婷, 2006, 不動產市場從眾行為表現與驗證, 國立中正大學財務管理研究所碩士論文 游和正, 2012, 領域相關詞彙極性分析及文件情緒分類之研究, 國立台灣大學資訊工程學系碩士論文 程于芳, 2006, 影響不動產市場之從眾行為與總體經濟因素之研究, 國立政治大學地政學系碩士論文 馮正民、曾平毅、王冠斐, 1994, 捷運系統對車站地區房價之影響, 都市與計劃,第二十一卷,第一期,pp.25-45 楊昌樺、陳信希, 2006, 以部落格文本進行情緒分類之研究, 自然語言與語音處理研討會 楊思聰, 2007, 內湖捷運對房價之影響,國立中央大學產業經濟研究碩士論文 楊謙柔, 2009, 都市環境設施評價模式之研究, 中國文化大學建築及都市計畫研究所博士論文 鄒函升, 2014, 新聞輿情偵測追蹤與民意關聯之研究-大資料之研究取向, 國立政治大學資訊管理學系碩士論文 蔡爾逸, 2012, 應用支撐向量機(SVM)於都市不動產價格預測之研究, 國立中央大學營建管理研究所碩士論文 蔡鎮宇, 2012, 社群情緒指標於房地產市場價格關聯之研究, 國立交通大學管理學院資訊管理學程碩士論文 蕭博文,2009, 以SVM為基礎之偽鈔辨識,國立台灣科技大學電機工程系碩士論文 B. Wuthrich , V. Cho , S. Leung , D. Permunetilleke , K. Sankaran , J. Zhang , W. Lam. 1998. Daily Stock Market Forecast from Textual Web Data. IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS Bo Pang & Lillian Lee. 2005. Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales. Proceedings of the Association for Computational Linguistics (ACL), 115–124. Bo Pang & Lillian Lee. 2008. Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135. Bollen, Johan, Huina Mao, and Xiao-Jun Zeng. 2010. Twitter mood predicts the stock market. arXiv preprint arXiv:1010.3003. C. Cortes and V. Vapnik.1995. Support vector networks. Machine Learning, 20:125 Derek J. de Solla Price. 1963. Little science, big science. New York: Columbia University Press. Duncan, J. W., & Sheridan, P. D. 2007. Influentials, networks, and public opinion formation. Journal of Consumer Research, 34(4), 441-58. Frame, Baum, Card. 1979. An information approach to examining developments in an energy technology: Coal gasification. Journal of the American Society for Information Science. Hickling Lewis Brod, Inc. 2002 , Commercial Property Benefits of Transit , Transportation Research Board,pp.1-48. Howard Rheingold.1993. The Virtual Community: Homesteading on the Electronic Frontier. London: MIT Hsu, C. W., Chang, C. C., Lin, C. J. 2003. A Practical Guideto Support Vector, Classification. Available at http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf. Hu, M., & Liu, B. 2004. Mining and summarizing customer reviews. Paper presented at the Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining. Jonathan Milgram, Mohamed Cheriet, Robert Sabourin. 2006. One Against One” or ”One Against All”: Which One is Better for Handwriting Recognition with SVMs?. Guy Lorette. Tenth International Workshop on Frontiers in Handwriting Recognition, Oct 2006, La Baule (France) Ku, L.-W. & Chen, H.-H. 2007. Mining opinions from the web: beyond relevance retrieval. Journal of American Society for Information Science and Technology, 58(12), 1838-850. L.-W. Ku and H.-H. Chen. 2007. Mining opinions from the Web: Beyond relevance retrieval. Journal of the American Society for Information Science and Technology, vol. 58, no. 12, pp. 1838-1850. Lun-Wei Ku and Hsin-Hsi Chen. 2007. Mining opinions from the Web: Beyond relevance retrieval. Journal of the American Society for Information Science and Technology 58. Pang, Bo, Lillian Lee, and Shivakumar Vaithyanathan. 2002. Thumbs up? Sentiment Classification using Machine Learning Techniques. Empirical methods in natural language processing - Volume 10 Pages 79-86 Shiller, Robert J. 2007. Understanding Recent Trends in House Prices and Homeownership. NBER Working Paper No. 13553 Smith, B. A., and W. P. Tesarek. 1991. House Prices and Regional Real Estate Cycles: Market Adjustments in Houston. Journal of the American Real Estate and Urban Economics Association 19, 396-416. Su, C. W., and Lin, C. J. 2002. A Comparison of Methods for Multi-class Support Vector Machines, Proceedings of IEEE Transactions on Neural Networks, pp.415-425. T. Mullen and N. Collier 2004. Sentiment analysis using support vector machines with diverse information sources. In Proceedings of Conference on Empirical Methods in Natural Language Processing. Vladimir Vapnik , Steven E. Golowich , Alex Smola. 1997. Support Vector Method for Function Approximation, Regression Estimation, and Signal Processing. Neural Information Processing Systems Conference |
Description: | 碩士 國立政治大學 資訊管理研究所 102356025 103 |
Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0102356025 |
Data Type: | thesis |
Appears in Collections: | [資訊管理學系] 學位論文
|
Files in This Item:
File |
Description |
Size | Format | |
602501.pdf | | 4511Kb | Adobe PDF2 | 114 | View/Open |
|
All items in 政大典藏 are protected by copyright, with all rights reserved.
|