DocumentCode :
2513570
Title :
Exploiting Combined Multi-level Model for Document Sentiment Analysis
Author :
Li, Si ; Zhang, Hao ; Xu, Weiran ; Chen, Guang ; Guo, Jun
Author_Institution :
Sch. of Inf. & Commun. Eng., Beijing Univ. of Posts & Telecommun., Beijing, China
fYear :
2010
fDate :
23-26 Aug. 2010
Firstpage :
4141
Lastpage :
4144
Abstract :
This paper focuses on the task of text sentiment analysis in hybrid online articles and web pages. Traditional approaches of text sentiment analysis typically work at a particular level, such as phrase, sentence or document level, which might not be suitable for the documents with too few or too many words. Considering every level analysis has its own advantages, we expect that a combination model may achieve better performance. In this paper, a novel combined model based on phrase and sentence level´s analyses and a discussion on the complementation of different levels´ analyses are presented. For the phrase-level sentiment analysis, a newly defined Left-Middle-Right template and the Conditional Random Fields are used to extract the sentiment words. The Maximum Entropy model is used in the sentence-level sentiment analysis. The experiment results verify that the combination model with specific combination of features is better than single level model.
Keywords :
Web sites; text analysis; conditional random fields; document sentiment analysis; hybrid online articles; left-middle-right template; maximum entropy model; multilevel model; phrase; sentence-level sentiment analysis; text sentiment analysis; web pages; Analytical models; Classification algorithms; Entropy; Feature extraction; Information retrieval; Syntactics; Text analysis; combined multi-level model; document-level; phrase-level; sentence-level; sentiment analysis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Pattern Recognition (ICPR), 2010 20th International Conference on
Conference_Location :
Istanbul
ISSN :
1051-4651
Print_ISBN :
978-1-4244-7542-1
Type :
conf
DOI :
10.1109/ICPR.2010.1007
Filename :
5597730
Link To Document :
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