基于SVM的微博情感傾向性分析研究
[Abstract]:With the advent of the Web2.0 era, the development of the network has entered all areas of people's lives. In recent years, the emergence of Weibo has made life more abundant. The growth of Weibo's influence has attracted a large number of scholars to study Weibo customers in depth, and emotional word recognition and emotional analysis have become an important topic. In Weibo's open platform, the function is to access information, or to post information to others to see. At the same time, with the diversification of published information, new problems arise, such as the emergence of emotional neologisms and the analysis of emotional polarity of Weibo sentences. The emergence of new words produces a lot of "scattered strings" and "fragments" that are difficult to recognize for Chinese word segmentation. The text that distinguishes the emotional tendency of Weibo guest in emotional analysis belongs to the judge of positive, negative and neutral. The emotional tendency of these texts can master the emotions of netizens, not only have certain commercial value, but also benefit the society, but also help us to perfect in the fields of public opinion monitoring, vocabulary updating, natural language processing and so on. Tens of thousands of Chinese Weibo users refresh their information every day, and the generation of Weibo emotional words and the analysis of polarity all arise. It is very important and urgent to do a good job in understanding the attitude of users. Through the data provided by the experiment, emotional word recognition through conditional random field, part-of-speech tagging, combining with the characteristics of context information, the feature vector is constructed, and the training model of corpus data is constructed and tested. Finally, the correct (Precision), recall rate (Recall) and F-value of emotional words are obtained. Effective and correct recognition of Weibo emotional words is the premise and basis for judging the emotional tendency of Weibo text. Based on the knowledge of Chinese information processing and natural language, combined with the laboratory research on the discovery of emotional neologisms and the analysis of emotional tendencies, this paper discusses the various relationships related to emotional tendencies. It is based on the existing analysis of Weibo emotional tendency judgment. The ultimate purpose of this paper is to improve the accuracy, recall rate and F-value of the data results, so as to lay a foundation for further research. The experimental data are different from the Weibo corpus given by the project, and the training and test data of emotional word recognition and emotional tendency analysis are different. The experimental results also verify that the method used in this paper is feasible. The experimental results show that the correct rate of emotional word recognition is 34.21%, the recall rate is 0.11%, and the F value is 0.002%. The results show that the overall recognition rate is not high, but it also lays a good foundation for the next step. The correct rate, recall rate and F value of emotional sentence polarity discrimination were 84.87%, 65.18% and 77.27%, respectively. the emotional tendency of Chinese Weibo was preliminarily explored in this study.
【學位授予單位】:中原工學院
【學位級別】:碩士
【學位授予年份】:2014
【分類號】:TP391.1;TP393.092
【相似文獻】
相關期刊論文 前10條
1 鄧森;楊軍鋒;郭明威;郭創(chuàng);;基于模糊SVM和虛擬儀器的模擬電路故障診斷研究[J];計算機測量與控制;2011年04期
2 郭有貴;曾萍;朱建林;;交-交矩陣變換器SVM的新穎調(diào)制模式(英文)[J];系統(tǒng)仿真學報;2009年22期
3 吳學文;索麗生;王志堅;;基于SVM的入庫徑流混沌時間序列預測模型及應用[J];系統(tǒng)仿真學報;2011年11期
4 程博,吳國平;基于SVM的脫機手寫漢字識別[J];現(xiàn)代計算機;2005年09期
5 鐘明霞;;基于神經(jīng)網(wǎng)絡和SVM的微鈣化簇分類方法[J];計算機時代;2008年05期
6 宋國明;王厚軍;姜書艷;劉紅;;一種聚類分層決策的SVM模擬電路故障診斷方法[J];儀器儀表學報;2010年05期
7 張淑雅;趙一鳴;李均利;;基于SVM的圖像分類算法與實現(xiàn)[J];計算機工程與應用;2007年25期
8 宋國明;王厚軍;劉紅;姜書艷;;基于提升小波變換和SVM的模擬電路故障診斷[J];電子測量與儀器學報;2010年01期
9 王志明,蔣加伏,唐賢瑛;基于SVM的小波圖像去噪[J];湖南科技學院學報;2005年05期
10 解焱陸,吳禮福,戴蓓劏,李輝;基于SVM評分融合的分類短語音話者確認系統(tǒng)[J];數(shù)據(jù)采集與處理;2005年02期
相關會議論文 前10條
1 滕衛(wèi)平;胡波;滕舟;鐘元;;SVM回歸法在西太平洋熱帶氣旋路徑預報中的應用研究[A];S1 災害天氣研究與預報[C];2012年
2 王紅軍;徐小力;付瑤;;基于SVM的旋轉機械故障診斷知識獲取[A];第八屆全國設備與維修工程學術會議、第十三屆全國設備監(jiān)測與診斷學術會議論文集[C];2008年
3 陳兆基;楊宏暉;杜方鍵;;用于水下目標識別的選擇性SVM集成算法[A];中國聲學學會水聲學分會2011年全國水聲學學術會議論文集[C];2011年
4 程麗麗;張健沛;楊靜;馬駿;;一種改進的層次SVM多類分類方法[A];第三屆全國信息檢索與內(nèi)容安全學術會議論文集[C];2007年
5 左南;李涓子;唐杰;;基于SVM的肖像照片抽取[A];第三屆全國信息檢索與內(nèi)容安全學術會議論文集[C];2007年
6 寧偉;苗雪雷;胡永華;季鐸;張桂平;蔡東風;;基于SVM的無參考譯文的譯文質(zhì)量評測[A];機器翻譯研究進展——第四屆全國機器翻譯研討會論文集[C];2008年
7 劉旭;羅鵬飛;李綱;;基于擬合角特征及SVM的雷達輻射源個體識別[A];全國第五屆信號和智能信息處理與應用學術會議?(第一冊)[C];2011年
8 羅浩;謝軍龍;胡云鵬;;地源熱泵空調(diào)系統(tǒng)故障診斷中SVM的應用[A];全國暖通空調(diào)制冷2008年學術年會資料集[C];2008年
9 劉閃電;王建東;;權重部分更新的大規(guī)模線性SVM求解器[A];2009年研究生學術交流會通信與信息技術論文集[C];2009年
10 王艦;湯光明;;基于SVM的圖像隱寫檢測分析[A];第八屆全國信息隱藏與多媒體安全學術大會湖南省計算機學會第十一屆學術年會論文集[C];2009年
相關碩士學位論文 前10條
1 張漢女;基于SVM的海岸線提取方法研究[D];東北師范大學;2010年
2 劉軍;基于SVM的半監(jiān)督網(wǎng)絡入侵檢測系統(tǒng)[D];復旦大學;2009年
3 張永俊;基于SVM的增量入侵檢測方法研究[D];西安科技大學;2013年
4 田冪;基于概率SVM的腫瘤預警系統(tǒng)的設計與實現(xiàn)[D];吉林大學;2013年
5 王碩;基于廣義S變換和SVM的電壓暫降檢測與識別方法研究[D];燕山大學;2013年
6 楊濤;基于SVM的中國醫(yī)藥制造企業(yè)財務危機預警研究[D];廈門大學;2009年
7 周洪利;基于SVM的網(wǎng)絡信息過濾研究[D];山東師范大學;2008年
8 齊振東;基于SVM的地基土承載力預測[D];吉林大學;2008年
9 任瓊;基于SVM的余杭生態(tài)公益林類型的遙感分類研究[D];南京林業(yè)大學;2008年
10 楊洋;基于SVM的印刷品缺陷在線檢測[D];華中科技大學;2012年
本文編號:2475396
本文鏈接:http://www.lk138.cn/guanlilunwen/ydhl/2475396.html