Home > Papers

 
 
Sentence-level Sentiment Analysis Based on Sub-view Framework
Xing Xinyan 1 * #,Zhang Xianchao 1,Liu Xiaohua 2
1.Software School of Dalian University of Technology
2.Microsoft Research Asia
*Correspondence author
#Submitted by
Subject:
Funding: none
Opened online:25 October 2010
Accepted by: none
Citation: Xing Xinyan,Zhang Xianchao,Liu Xiaohua.Sentence-level Sentiment Analysis Based on Sub-view Framework[OL]. [25 October 2010] http://en.paper.edu.cn/en_releasepaper/content/4388143
 
 
This paper presents a method for improving the performance of sentence-level sentiment analysis. Sentiment analysis is thought to require a deep understanding of the sentence structure (e.g., word order and non-local dependency). To attack this problem without the sentence parsing, we propose an approach whereby a given sentence is decomposed into a series of sub-sequences or sub-view representations. Sentence-level polarity is then determined by classifying within sub-views and fusing the obtained sub-view polarities. Two specific methods are instantiated: stacking-based maximum entropy model and hidden conditional random fields (HCRFs) based on contextual features. Extensive evaluations were carried out on two benchmark dataset, one is for sentence subjectivity classification and the other is for sentence polarity detection. Experimental results show that the performance of our proposed method is comparable to the state-of-the-art approaches.
Keywords:Natural Language Processing; Sentiment Analysis; Sequence Model
 
 
 

For this paper
  • ● PDF (0B)
  • ● Revision 0 　　
  • ● Print this paper
  • ● Recommend this paper to a friend
  • ● Add to my favorite list

    Saved Papers

    Please enter a name for this paper to be shown in your personalized Saved Papers list

Tags
Add yours

Related Papers

Statistics
PDF Downloaded 450
Bookmarked 0
Recommend 5
Comments Array
Submit your papers