Open AccessA GRU Model for Aspect Level Sentiment Analysis
Author(s) -
Yongping Xing,
Chuangbai Xiao
Publication year - 2019
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 - 10.1088/1742-6596/1302/3/032042
Subject(s) - sentiment analysis , sentence , computer science , fuse (electrical) , artificial intelligence , natural language processing , polarity (international relations) , relation (database) , task (project management) , feature (linguistics) , key (lock) , mechanism (biology) , data mining , linguistics , engineering , philosophy , genetics , computer security , systems engineering , biology , electrical engineering , cell , epistemology
Sentiment analysis is a basic task of natural language processing, while aspect level sentiment analysis is an important topic in sentiment analysis. In the same sentence, different words have different influence on the sentiment polarity of aspect, so the key to solve the problem is how to build a relation model between the aspect and the words in the sentence. In this paper, by using two recurrent networks, we built a model for sentence and introduced attention mechanism to fuse aspect information, so as to achieve a better effect. An experiment on public dataset show that the proposed algorithm obtain a better result without carrying out complex feature engineering.