Aspect-level Sentiment Analysis Based on Effective Distance and Attention Mechanism
LIU Yue
ZHANG Kun
ZHU Haohua
JIANG Haojun
FANG Zizheng
Abstract：At present,location-based aspect sentiment analysis mainly studies aspect words and other words based on seman-tic position,but this method is prone to unreasonable word weight allocation,which reduces the accuracy of sentiment analysis.Therefore,a new emotion analysis model,ED-BERT-BIGRU(EDBB),is proposed by studying the grammatical aspects of sen-tences.The model firstly inputs three kinds of texts into BERT model to obtain corresponding word vectors,and uses bidirectional GRU to capture context representation.Then it uses rely on syntax tree in each word and the word of the relative position,combining with the calculating CED algorithm to obtain the effective distance each word and the word of the effective distance,at the same time,combined with local text weight LDAW dynamic assignment algorithm and the attention mechanism for each word gives differ-ent weights,so as to improve the accuracy of sentiment analysis.Compared with other deep neural network models,this model achieves higher Accuracy and F1 values on the Restaurant and Laptop public data sets of Semeval-2014,and the training results of the model are better.
Keywords：relative positioneffective distanceCED algorithmLDAW algorithm
Publication Date：2025-03-20
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：7( 637-642,707 )