Modeling Sentiment Dependencies with Graph Convolutional Networks for Aspect-level Sentiment Classification
Aspect-level sentiment classification aims to distinguish the sentiment polarities over one or more aspect terms in a sentence. Existing approaches mostly model different aspects in one sentence independently, which ignore the sentiment dependencies between different aspects. However, we find such dependency information between different aspects can bring additional valuable information. In this paper, we propose a novel aspect-level sentiment classification model based on graph convolutional networks (GCN) which can effectively capture the sentiment dependencies between multi-aspects in one sentence. Our model firstly introduces bidirectional attention mechanism with position encoding to model aspect-specific representations between each aspect and its context words, then employs GCN over the attention mechanism to capture the sentiment dependencies between different aspects in one sentence. We evaluate the proposed approach on the SemEval 2014 datasets. Experiments show that our model outperforms the state-of-the-art methods. We also conduct experiments to evaluate the effectiveness of GCN module, which indicates that the dependencies between different aspects is highly helpful in aspect-level sentiment classification.
NurtureToken New!

Token crowdsale for this paper ends in

Buy Nurture Tokens

Authors

Are you an author of this paper? Check the Twitter handle we have for you is correct.

Pinlong Zhaoa (add twitter)
Linlin Houb (add twitter)
Ou Wua (add twitter)
Ask The Authors

Ask the authors of this paper a question or leave a comment.

Read it. Rate it.
#1. Which part of the paper did you read?

#2. The paper contains new data or analyses that is openly accessible?
#3. The conclusion is supported by the data and analyses?
#4. The conclusion is of scientific interest?
#5. The result is likely to lead to future research?

Github
Repo:
Stargazers:
1
Forks:
0
Open Issues:
0
Network:
0
Subscribers:
0
Language:
Python
Modeling Sentiment Dependencies with Graph Convolutional Networks for Aspect-level Sentiment Classification
Youtube
Link:
None (add)
Views:
0
Likes:
0
Dislikes:
0
Favorites:
0
Comments:
0
Other
Sample Sizes (N=):
Inserted:
Words Total:
Words Unique:
Source:
Abstract:
None
06/11/19 06:03PM
6,724
1,950
Tweets
arxiv_cscl: Modeling Sentiment Dependencies with Graph Convolutional Networks for Aspect-level Sentiment Classification https://t.co/f40YlwN8rX
arxiv_cscl: Modeling Sentiment Dependencies with Graph Convolutional Networks for Aspect-level Sentiment Classification https://t.co/f40Ylx4JQx
Memoirs: Modeling Sentiment Dependencies with Graph Convolutional Networks for Aspect-level Sentiment Classification. https://t.co/CkpzFNUyS8
arxiv_cs_LG: Modeling Sentiment Dependencies with Graph Convolutional Networks for Aspect-level Sentiment Classification. Pinlong Zhaoa, Linlin Houb, and Ou Wua https://t.co/iNra79Csnq
BrundageBot: Modeling Sentiment Dependencies with Graph Convolutional Networks for Aspect-level Sentiment Classification. Pinlong Zhaoa, Linlin Houb, and Ou Wua https://t.co/4ammJX1jsG
arxiv_cscl: Modeling Sentiment Dependencies with Graph Convolutional Networks for Aspect-level Sentiment Classification https://t.co/f40Ylx4JQx
Images
Related