Please wait a minute...
浙江大学学报(工学版)
J4  2011, Vol. 45 Issue (7): 1167-1174    DOI: 10.3785/j.issn.1008-973X.2011.07.005
    
Survey on sentiment orientation analysis of texts
LI Xiao-jun1, DAI Lin1, SHI Han-xiao1, HUANG Qi2
1.School of Computer Science and Information Engineering, Zhejiang Gongshang University, Hangzhou 310018, China;
2. College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
Download:   PDF(0KB) HTML
Export: BibTeX | EndNote (RIS)      

Abstract  

The basic flow of sentiment orientation analysis of texts was introduced, and the primary four aspects of current interesting researches were presented: subjectivity text recognition, sentiment orientation analysis method of texts, existing systems and evaluation methods, construction of corpus. Then three methods and their characteristics were summarized, i.e. simple statistics, machine learning and fine-grained sentiment relative analysis method. Merits and demerits of methods were analyzed from complexity, efficiency and applicable scope. Finally, the current achievements and shortages were summarized, and forecasted research perspectives were proposed including basic problem and implementation method of specific application．

Published: 01 July 2011
CLC:  TP 18  
  TP 391  
Service
E-mail this article
Add to my bookshelf
Add to citation manager
E-mail Alert
RSS
Articles by authors
Cite this article:

LI Xiao-jun, DAI Lin, SHI Han-xiao, HUANG Qi. Survey on sentiment orientation analysis of texts. J4, 2011, 45(7): 1167-1174.

URL:

https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2011.07.005     OR     https://www.zjujournals.com/eng/Y2011/V45/I7/1167


文本倾向性分析综述

介绍文本倾向性分析的基本流程,从主观性文本识别、文本倾向性分析方法、现有系统及评测方法、语料库建设4个方面对现有文本倾向性分析技术进行介绍和概括.综述了文本倾向性分析的3类研究方法：简单统计方法、机器学习方法和细粒度情感相关性分析方法,分析这3类研究方法的特点,从算法复杂性、效率和适用范围等方面比较各自的优缺点.概括现有研究的成就和不足,从基础性问题、具体应用的实现方法2个方面提出研究的前景.

［1］ 姚天昉,程希文,徐飞玉,等. 文本意见挖掘综述［J］.中文信息学报,2008,22（3）：71-80．
YAO Tianfang, CHENG Xiwen, XU Feiyu, et al. A survey of opinion mining for texts ［J］. Journal of Chinese Information Processing, 2008, 22(3): 71-80．
［2］ 周立柱,贺宇凯,王建勇.情感分析研究综述［J］.计算机应用,2008,28（11）：2725-2728．
ZHOU Lizhu, HE Yukai, WANG Jianyong. Survey on research of sentiment analysis ［J］. Computer Application,2008,28(11): 2725-2728．
［3］ HATZIVASSILOGLOU V, WIEBE J M. Effects of adjective orientation and gradability on sentence subjectivity ［C］∥ Proceedings of the 18th Conference on Computational Linguistics. USA: ACL, 2000: 299-305．
［4］ FINN A, KUSHMERICK N, SMYTH B. Genre classification and domain transfer for information filtering ［C］∥Proceedings of the 24th BCSIRSG European Colloquium on Information Retrieval Research: Advances in Information Retrieval. UK: Springer, 2002: 353-362．
［5］ WIEBE J, BRUCE R, BELL M, et al. A corpus study of evaluative and speculative language ［C］∥Proceedings of the 2nd ACL SIGdial Workshop on Discourse and Dialogue. USA: ACL, 2001: 1-10．
［6］ YU H, HATZIVASSILOGLOU V. Towards answering opinion questions: separating facts from opinions and identifying the polarity of opinion sentences ［C］∥Proceedings of the 2003 Conference on EMNLP. USA: ACL, 2003: 129-136．
［7］ BRUCE R F, WIEBE J M. Recognizing subjectivity: a case study in manual tagging ［J］. Natural Language Engineering, 1999, 5(2):187-205．
［8］ WIEBE J, RILOFF E. Creating subjective and objective sentence classifiers from unannotated texts ［C］∥Proceedings of the 6th International Conference on Intelligent Text Processing and Computational Linguistics. Germany: Springer, 2005: 475-486．
［9］ WILSON T, HOFFMANN P, SOMASUNDARAN S, et al. OpinionFinder: a system for subjectivity analysis ［C］ ∥Proceedings of HLT/EMNLP 2005 Interactive Demonstrations. Morristown, NJ, USA: ACL， 2005: 34-35．
［10］ PANG B, LEE L. A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts ［C］∥Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics. Morristown, NJ, USA: ACL, 2004: 271-278．
［11］ 叶强,张紫琼,罗振雄. 面向互联网评论情感分析的中文主观性自动判别方法研究［J］.信息系统学报,2007,1(1)：79-91．
YE Qiang, ZHANG Ziqiong, LUO Zhenxiong. Automatically measuring subjectivity of chinese sentences for sentiment analysis to reviews on the internet ［J］.China Journal of Information Systems, 2007,1(1)：79-91．
［12］ 来火尧,刘功申. 基于主题相关性分析的文本倾向性研究［J］.信息安全与通信保密,2009（3）: 77-78．
LAI Huoyao, LIU Gongshen. Prediction on semantic orientation of texts based on topic correlation ［J］. China Information Security,2009（3）: 77-78．
［13］ STONE P J, DUNPHY D C, SMITH M S, et al. The general inquirer: a computer approach to content analysis ［M］. Cambridge, MA, USA: MIT, 1966: 1-6．
［14］ KAMPS J, MARX M, MOKKEN R J, et al. Using WordNet to measure semantic orientations of adjectives ［C］∥ Proceedings of the 4th International Conference on Language Resources and Evaluation. Lisbon, Portugal: ［s.n.］, 2004: 1115-1118．
［15］ 朱嫣岚,闵锦,周雅倩,等. 基于HowNet的词汇语义倾向计算.中文信息学报,2006,20(1): 14-20．
ZHU Yanlan, MIN Jin, ZHOU Yaqian, et al. Semantic orientation computing based on HowNet ［J］. Journal of Chinese Information Processing, 2006, 20(1): 14-20．
［16］ 熊德兰,程菊名,田胜利. 基于HowNet的句子褒贬倾向研究［J］.计算机工程与应用,2008,44(22)：143-145．
XIONG Delan, CHENG Juming, TIAN Shengli. Sentence orientation research based on HowNet ［J］. Computer Engineering and Application, 2008, 44(22): 143-145．
［17］ TURNEY P D. Thumbs up or thumbs down？ semantic orientation applied to unsupervised classification of reviews ［C］∥ Proceedings of the 40th Annual Meeting on Association for Computational Linguistics. Pennsylvania, USA: ACL, 2002: 417-424．
［18］ TURNEY P D, LITTMAN M L. Measuring praise and criticism: Inference of semantic orientation from association ［J］. ACM Transactions on Information Systems, 2003, 2
[1] YU Jun, WANG Zeng-fu. Video stabilization based on empirical mode decomposition and
several evaluation criterions[J]. J4, 2014, 48(3): 423-429.
[2] LIU Ye-feng, XU Guan-qun, PAN Quan-ke, CHAI Tian-you. Magnetic material molding sintering production scheduling optimization method and its application[J]. J4, 2013, 47(9): 1517-1523.
[3] LIN Yi-ning, WEI Wei, DAI Yuan-ming. Semi-supervised Hough Forest tracking method[J]. J4, 2013, 47(6): 977-983.
[4] HAO Chuan-chuan, FANG Zhou, LI Ping. Output feedback reinforcement learning control method
based on reference model[J]. J4, 2013, 47(3): 409-414.
[5] WANG Peng-jun, WANG Zhen-hai, CHEN Yao-wu, LI Hui. Searching the best polarity for fixed polarity Reed-Muller
circuits based on delay model[J]. J4, 2013, 47(2): 361-366.
[6] LI Kan, HUANG Wen-xiong, HUANG Zhong-hua. Multi-sensor detected object classification method based on
support vector machine[J]. J4, 2013, 47(1): 15-22.
[7] XIAO Dong-feng, YANG Chun-jie，SONG Zhi-huan. The forecasting model of blast furnace gas output
based on improved BP network[J]. J4, 2012, 46(11): 2103-2108.
[8] YAO Fu-tian, QIAN Yun-tao, LI Ji-ming. Semi-supervised learning based Gaussian processes for
hyperspectral image classification[J]. J4, 2012, 46(7): 1295-1300.
[9] WANG Hong-bo, ZHAO Guang-zhou, QI Dong-lian, LU Da. Fast incremental learning method for one-class support vector machine[J]. J4, 2012, 46(7): 1327-1332.
[10] AI Jie-qing, GAO Ji, PENG Yan-bin, ZHENG Zhi-jun. Negotiation decision model based on transductive
support vector machine[J]. J4, 2012, 46(6): 967-973.
[11] PAN Jun, KONG Fan-sheng, WANG Rui-qin. Locality sensitive discriminant transductive learning[J]. J4, 2012, 46(6): 987-994.
[12] JIN Zhuo-jun, QIAN Hui, ZHU Miao-liang. Trajectory evaluation method based on intention analysis[J]. J4, 2011, 45(10): 1732-1737.
[13] HU Bin,LI Yang,GAO Ji. Symbolic model checking based verification method
for trustworthy cross-organizational collaboration system[J]. J4, 2011, 45(9): 1558-1565.
[14] WANG Xiu-jun, HU Xie-he. An improved control strategy of single neuron PID[J]. J4, 2011, 45(8): 1498-1501.
[15] GU Hong, ZHAO Guang-zhou. Image retrieval and recognition based on generalized
local distance functions[J]. J4, 2011, 45(4): 596-601.