Paper Info
Title
A Dynamic Conditional Random Field Based Framework for Sentence-Level Sentiment Analysis of Chinese Microblog
Abstract
With the increasing popularity of social media, the Sentiment Analysis (SA) of the Microblog has raised as a new research topic. In this paper, we present WDCRF: a Word2vec and Dynamic Conditional Random Field (DCRF) based framework for Sentiment Analysis of Chinese Microblog. Our contributions include: firstly, to address drawbacks of Microblog message such as the length and Lexicon limitations, Word2vec technology is leveraged to enrich Microblog message so that each word individual is extended by its Top-k similar words. Secondly, DCRF model is utilized to combine and conduct the Subjectivity Classification and Polarity Classification simultaneously, while in existing works they are designed as independent and the relationship between two types of classifications is ignored. Moreover, the DCRF model considers not only the classification-level relationship but also the relationship between neighboring sentences. Finally, the experiments on real dataset collected from Sina and Tencent Weibo demonstrate that our WDCRF (Word2vec + DCRF) achieves much better than the state-of-the-art.
Year
DOI
Venue
2017
10.1109/CSE-EUC.2017.33
2017 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC)
Keywords
Field
DocType
Chinese Microblog,Sentiment Analysis,Word2vec,Dynamic Conditional Random Fields
Data mining,Computer science,Popularity,Natural language processing,Artificial intelligence,Distributed computing,Conditional random field,Social media,Sentiment analysis,Microblogging,Support vector machine,Lexicon,Word2vec,Sentence
Conference
Volume
ISSN
ISBN
1
1949-0828
978-1-5386-3222-2
Citations 
PageRank 
References 
0
0.34
23
Authors
5
Name
Order
Citations
PageRank
Zhifeng Hao165378.36
Ruichu Cai224137.07
Yiyang Yang300.34
Wen Wen483.51
Lixin Liang500.34