Paper Info
Title
Convolution-based Memory Network for Aspect-based Sentiment Analysis.
Abstract
Memory networks have shown expressive performance on aspect based sentiment analysis. However, ordinary memory networks only capture word-level information and lack the capacity for modeling complicated expressions which consist of multiple words. Targeting this problem, we propose a novel convolutional memory network which incorporates an attention mechanism. This model sequentially computes the weights of multiple memory units corresponding to multi-words. This model may capture both words and multi-words expressions in sentences for aspect-based sentiment analysis. Experimental results show that the proposed model outperforms the state-of-the-art baselines.
Year
DOI
Venue
2018
10.1145/3209978.3210115
SIGIR
Keywords
Field
DocType
sentiment analysis,memory network,convolutional operation
Data mining,Expression (mathematics),Convolution,Sentiment analysis,Computer science
Conference
ISBN
Citations 
PageRank 
978-1-4503-5657-2
2
0.37
References 
Authors
8
6
Name
Order
Citations
PageRank
Chuang Fan141.45
Qinghong Gao240.73
Du Jiachen3369.02
Lin Gui4186.43
Xu Ruifeng543253.04
Kam-Fai Wong61718176.33