An empirical study of sentence features for subjectivity and polarity classification

被引:55
作者
Chenlo, Jose M. [1 ]
Losada, David E. [1 ]
机构
[1] Univ Santiago de Compostela, Ctr Invest Tecnol Informac CITIUS, Santiago De Compostela, Corunna, Spain
关键词
Sentiment Analysis; Opinion Mining; Sentence-level analysis; Subjectivity classification; Polarity classification;
D O I
10.1016/j.ins.2014.05.009
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
While a number of isolated studies have analysed how different sentence features are beneficial in Sentiment Analysis, a complete picture of their effectiveness is still lacking. In this paper we extend and combine the body of empirical evidence regarding sentence subjectivity classification and sentence polarity classification, and provide a comprehensive analysis of the relative importance of each set of features using data from multiple benchmarks. To the best of our knowledge, this is the first study that evaluates a highly diversified set of sentence features for the two main sentiment classification tasks. (C) 2014 Elsevier Inc. All rights reserved.
引用
收藏
页码:275 / 288
页数:14
相关论文
共 45 条
[1]  
[Anonymous], 2005, P HUM LANG TECHN C E
[2]  
[Anonymous], P 20 ACM INT C INF K
[3]  
[Anonymous], 2003, Proceedings of the Seventh Conference on Natural Language Learning at HLT-NAACL 2003-Volume 4, CONLL'03
[4]  
[Anonymous], 2002, Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
[5]  
[Anonymous], 2009, P 2009 C EMP METH NA
[6]  
[Anonymous], 2004, 20 INT C COMP LING G
[7]  
Beineke P., P AAAI SPR S EXPL AT, P12
[8]   Endorsements and rebuttals in blog distillation [J].
Berardi, Giacomo ;
Esuli, Andrea ;
Sebastiani, Fabrizio ;
Silvestri, Fabrizio .
INFORMATION SCIENCES, 2013, 249 :38-47
[9]  
Bo Pang, 2008, Foundations and Trends in Information Retrieval, V2, P1, DOI 10.1561/1500000001
[10]  
Cambria Erik, 2013, Advances in Soft Computing and Its Applications. 12th Mexican International Conference on Artificial Intelligence, MICAI 2013. Proceedings, LNCS 8266, P478, DOI 10.1007/978-3-642-45111-9_41