房價微博情感分類研究
發(fā)布時間：2018-09-07 15:57
【摘要】：房價滿意度作為衡量社會發(fā)展的一個重要指標，正在引起社會的廣泛關(guān)注但是由于難以量化數(shù)據(jù)收集繁瑣時效性弱等困難，相關(guān)研究無法深入伴隨著互聯(lián)網(wǎng)技術(shù)的不斷進步，在線討論平臺的快速發(fā)展壯大，如新浪微博主題論壇等，民眾利用這些新興渠道暢所欲言，在其中就包括與房價高度相關(guān)的大量言論信息這些信息背后就是民眾對于房價的情感態(tài)度，是民眾對于房價滿意度的一種碎片式表達，這些碎片化的信息中就包含著民眾對于房價的滿意程度房價微博情感分類，是指利用數(shù)據(jù)挖掘的方法，對大數(shù)量級的房價微博進行情感傾向信息識別，借此為房價滿意度研究提供支持 本文以北京房價微博作為直接研究對象首先，采集了以北京房價為關(guān)鍵字的，2011年1月到2014年1月這個時間段內(nèi)的所有微博數(shù)據(jù)，其中有效數(shù)據(jù)共計59957條然后，基于N-Gram語言模型構(gòu)建情感傾向分類器通過不斷優(yōu)化訓練集使分類準確率達到95%以上最后，在準確率達到要求的前提下，，挖掘出蘊含在房價微博中民眾對于房價的情感傾向 依據(jù)本文前兩章所取得的成果，對民眾滿意度與房價之間的關(guān)系進行實證分析首先，利用基于N-Gram語言模型的情感分類器對每月的北京房價微博數(shù)據(jù)進行情感傾向識別，計算情緒得分，借此量化民眾對于房價的滿意程度然后，聯(lián)系北京市每月的新建住宅銷售價格指數(shù)這一相對值住宅平均銷售價格這一絕對值，以及推算出的每月住宅銷售價格增長率這三個變量進行統(tǒng)計分析最終，統(tǒng)計分析結(jié)果表明民眾對于房價的滿意程度受到房價絕對值和相對值的顯著影響，且房價相對值對其影響程度更強，相比于房價絕對值進而，聯(lián)系所查閱文獻與相關(guān)理論進行模型結(jié)果的解釋最后，本研究利用所取得的成果，聯(lián)系房地產(chǎn)實踐領(lǐng)域，給予提高房地產(chǎn)領(lǐng)域民眾滿意度的建議本研究為中文文本情感傾向自動識別在房地產(chǎn)領(lǐng)域進行了新的探索，為政府制定公共政策提供數(shù)據(jù)支持和理論基礎(chǔ)，也為學者繼續(xù)研究文本情感傾向提供很好的思路
[Abstract]:As an important index to measure social development, house price satisfaction is attracting wide attention of the society. However, due to the difficulty of quantifying data collection, such difficulties as tedious and weak timeliness, the related research can not go deep with the continuous progress of Internet technology. With the rapid development of online discussion platforms, such as the Sina Weibo theme Forum, people use these new channels to speak freely. Among them is a large amount of speech information that is highly relevant to house prices. Behind this information is the public's emotional attitude towards housing prices, a fragmented expression of people's satisfaction with housing prices. These pieces of information contain the people's satisfaction with the housing prices, Weibo's emotional classification of housing prices, which refers to the use of data mining methods to identify the affective tendency information of the house prices in the order of magnitude, Weibo. In order to provide support for the research on the degree of house price satisfaction, this paper takes Weibo as the direct research object. Firstly, we collect all Weibo data in the period from January 2011 to January 2014, which is based on the key word of housing price in Beijing. There are 59957 valid data, and then, based on the N-Gram language model, the classification accuracy of emotion tendency classifier is over 95% by continuously optimizing the training set. Excavating the emotion tendency of the people to the house price in Weibo of housing price, according to the results obtained in the first two chapters of this paper, the relationship between the satisfaction of the people and the house price is analyzed empirically, first of all, The emotion classifier based on N-Gram language model is used to identify the emotion tendency of Weibo data of housing price in Beijing every month, to calculate the emotion score, so as to quantify the people's satisfaction with the house price, and then, Connecting with the absolute value of the monthly sales price index of newly built residential buildings in Beijing, the absolute value of the average residential sales price, and the calculated monthly residential sales price growth rate, the three variables are statistically analyzed. The results of statistical analysis show that the satisfaction of the public with the house price is significantly affected by the absolute and relative value of the house price, and the relative value of the house price has a stronger impact on it, compared with the absolute value of the house price, With reference to literature and related theories to explain the results of the model finally, this study uses the results obtained, the real estate practice field, Suggestions for improving the satisfaction of people in Real Estate this study provides a new exploration for automatic identification of emotional tendencies in Chinese texts and provides data support and theoretical basis for the government to formulate public policies. It also provides a good way for scholars to continue to study the emotional tendency of text.
【學位授予單位】：哈爾濱工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：G206;F299.23
本文編號：2228718
[Abstract]:As an important index to measure social development, house price satisfaction is attracting wide attention of the society. However, due to the difficulty of quantifying data collection, such difficulties as tedious and weak timeliness, the related research can not go deep with the continuous progress of Internet technology. With the rapid development of online discussion platforms, such as the Sina Weibo theme Forum, people use these new channels to speak freely. Among them is a large amount of speech information that is highly relevant to house prices. Behind this information is the public's emotional attitude towards housing prices, a fragmented expression of people's satisfaction with housing prices. These pieces of information contain the people's satisfaction with the housing prices, Weibo's emotional classification of housing prices, which refers to the use of data mining methods to identify the affective tendency information of the house prices in the order of magnitude, Weibo. In order to provide support for the research on the degree of house price satisfaction, this paper takes Weibo as the direct research object. Firstly, we collect all Weibo data in the period from January 2011 to January 2014, which is based on the key word of housing price in Beijing. There are 59957 valid data, and then, based on the N-Gram language model, the classification accuracy of emotion tendency classifier is over 95% by continuously optimizing the training set. Excavating the emotion tendency of the people to the house price in Weibo of housing price, according to the results obtained in the first two chapters of this paper, the relationship between the satisfaction of the people and the house price is analyzed empirically, first of all, The emotion classifier based on N-Gram language model is used to identify the emotion tendency of Weibo data of housing price in Beijing every month, to calculate the emotion score, so as to quantify the people's satisfaction with the house price, and then, Connecting with the absolute value of the monthly sales price index of newly built residential buildings in Beijing, the absolute value of the average residential sales price, and the calculated monthly residential sales price growth rate, the three variables are statistically analyzed. The results of statistical analysis show that the satisfaction of the public with the house price is significantly affected by the absolute and relative value of the house price, and the relative value of the house price has a stronger impact on it, compared with the absolute value of the house price, With reference to literature and related theories to explain the results of the model finally, this study uses the results obtained, the real estate practice field, Suggestions for improving the satisfaction of people in Real Estate this study provides a new exploration for automatic identification of emotional tendencies in Chinese texts and provides data support and theoretical basis for the government to formulate public policies. It also provides a good way for scholars to continue to study the emotional tendency of text.
【學位授予單位】：哈爾濱工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：G206;F299.23
【參考文獻】
相關(guān)期刊論文 前10條
1 汪志圣;李龍澍;;Web文檔分類方法的比較與分析[J];滁州學院學報;2007年06期
2 李艷玲;戴冠中;覃森;;快速的文本傾向性分類方法(英文)[J];電子科技大學學報;2007年06期
3 劉洪;王鳳嬌;;微博用戶信息傳播的心理需求研究[J];傳播與版權(quán);2013年01期
4 胡熠;陸汝占;李學寧;段建勇;陳玉泉;;基于語言建模的文本情感分類研究[J];計算機研究與發(fā)展;2007年09期
5 李寧寧,張春光;社會滿意度及其結(jié)構(gòu)要素[J];江蘇社會科學;2001年04期
6 柴玉梅;熊德蘭;昝紅英;;Web文本褒貶傾向性分類研究[J];計算機工程;2006年17期
7 王學靜;;高房價影響社會心理[J];科技與企業(yè);2010年04期
8 毛偉;徐蔚然;郭軍;;基于n-gram語言模型和鏈狀樸素貝葉斯分類器的中文文本分類系統(tǒng)[J];中文信息學報;2006年03期
9 姚天f ;婁德成;;漢語語句主題語義傾向分析方法的研究[J];中文信息學報;2007年05期
10 徐軍;丁宇新;王曉龍;;使用機器學習方法進行新聞的情感自動分類[J];中文信息學報;2007年06期
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