Title
Construction Of Microblog-Specific Chinese Sentiment Lexicon Based On Representation Learning
Abstract
Sentiment analysis is a research hotspot in Nature Language Processing, and high-quality sentiment lexicon plays an important part in sentiment analysis. In this paper, we explore an approach to build a microblog-specific Chinese sentiment lexicon from massive microblog data. In feature learning, in order to enhance the quality of word embedding, we build a neural architecture to train a sentiment-aware word embedding by integrating three kinds of knowledge, including the context words and their composing characters, the polarity of sentences and the polarity of labeled words. Experiments conducted on several public datasets show that in both unsupervised and supervised microblog sentiment classification, the lexicon generated by our approach achieves the state-of-the-art performance compared to several existing Chinese sentiment lexicons and our feature learning method successfully catches both semantics and sentiment information.
Year
DOI
Venue
2018
10.1007/978-3-319-97304-3_16
PRICAI 2018: TRENDS IN ARTIFICIAL INTELLIGENCE, PT I
Keywords
Field
DocType
Sentiment lexicon, Representation learning, Microblog
Architecture,Social media,Sentiment analysis,Computer science,Microblogging,Lexicon,Natural language processing,Artificial intelligence,Word embedding,Machine learning,Semantics,Feature learning
Conference
Volume
ISSN
Citations 
11012
0302-9743
0
PageRank 
References 
Authors
0.34
19
6
Name
Order
Citations
PageRank
Li Kong152.42
Chuanyi Li22712.92
JiDong Ge311928.39
Yufan Yang400.68
Feifei Zhang56119.93
Bin Luo66621.04