Open AccessA Fine-grained Sentiment Analysis Method Based on Dependency Tree and Graph Attention Network
Author(s) -
Yejin Tan,
Wangshu Guo,
Jiawei He,
Jian Liu,
Ming Xian
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1651/1/012173
Subject(s) - sentiment analysis , computer science , dependency (uml) , graph , the internet , scheme (mathematics) , dependency graph , data science , order (exchange) , social media , tree (set theory) , artificial neural network , social network (sociolinguistics) , artificial intelligence , data mining , machine learning , theoretical computer science , world wide web , mathematical analysis , mathematics , finance , economics
With the rapid development of the Internet, e-commerce and social media have continued to develop and grow. Merchants tend to better understand users’ attitudes and emotional tendencies through comments. As the number of user reviews increases, the previous sentiment analysis methods have highlighted the problems of high cost and high error rate. How to use more advanced methods to analyse comments has become an urgent problem to be solved. In order to solve the above problems, in this article, we propose a fine-grained sentiment analysis method based on dependency tree and graph neural network, which can help businesses and social network platforms to identify users’ sentiment tendencies and can be subsequently used in recommendation systems and public opinion analysis systems. The experimental results show that our scheme has achieved the best results.