Sentiment Analysis Model Based on Structure Attention Mechanism

2017 ◽  
pp. 17-27
Author(s):  
Kai Lin ◽  
Dazhen Lin ◽  
Donglin Cao
Keyword(s):  
Sentiment Analysis ◽  
Attention Mechanism ◽  
Analysis Model ◽  
Model Based

Sentiment Analysis Model Based on the Word Structural Representation

2021 ◽  
pp. 170-178
Author(s):  
Gulmira Bekmanova ◽  
Banu Yergesh ◽  
Altynbek Sharipbay
Keyword(s):  
Sentiment Analysis ◽  
Analysis Model ◽  
Structural Representation ◽  
Model Based

Towards a Sentiment Analysis Model Based on Semantic Relation Analysis

2018 ◽  
Vol 9 (2) ◽  
pp. 54-75 ◽  
Author(s):  
Thien Khai Tran ◽  
Tuoi Thi Phan
Keyword(s):  
Sentiment Analysis ◽  
Semantic Relation ◽  
Effective Model ◽  
Analysis Model ◽  
Model Based ◽  
Relation Analysis ◽  
Novel Approaches ◽  
Syntactic Dependency

Sentiment analysis is an important new field of research that has attracted the attention not only of researchers, but also businesses and organizations. In this article, the authors propose an effective model for aspect-based sentiment analysis for Vietnamese. First, sentiment dictionaries and syntactic dependency rules were combined to extract reliable word pairs (sentiment - aspect). They then relied on ontology to group these aspects and determine the sentiment polarity of each. They introduce two novel approaches in this work: 1) in order to “smooth” the sentiment scaling (rather than using discrete categories of 1, 0, and -1) for fined-grained classification, then extract multi-word sentiment phrases instead of sentiment words, and 2) the focus is not only on adjectives but also nouns and verbs. Initial evaluations of the system using real reviews show promising results.

scholarly journals Towards a sentiment analysis model based on semantic relation analysis

2018 ◽  
Vol 20 (K7) ◽  
pp. 21-27
Author(s):  
Thien Khai Tran ◽  
Tuoi Thi Phan
Keyword(s):  
Sentiment Analysis ◽  
Semantic Relation ◽  
Effective Model ◽  
Analysis Model ◽  
Model Based ◽  
Sentiment Dictionary ◽  
Relation Analysis ◽  
Syntactic Dependency ◽  
Positive Results

In this paper, we propose an effective model for aspect-based sentiment analysis. First, we combined a sentiment dictionary and syntactic dependency rules to extract reliable word pairs (sentiment — aspect). Then, thanks to ontology, we grouped those aspects and determined the sentiment polarity of each. When we conducted experiments on real reviews, the system showed positive results.

scholarly journals A Multichannel Model for Microbial Key Event Extraction Based on Feature Fusion and Attention Mechanism

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Peng Li ◽  
Qian Wang
Keyword(s):  
Sentiment Analysis ◽  
Feature Fusion ◽  
Attention Mechanism ◽  
Event Extraction ◽  
Semantic Features ◽  
Analysis Model ◽  
Global Features ◽  
Public Health Emergencies ◽  
Multimodal Information ◽  
Local And Global Features

In order to further mine the deep semantic information of the microbial text of public health emergencies, this paper proposes a multichannel microbial sentiment analysis model MCMF-A. Firstly, we use word2vec and fastText to generate word vectors in the feature vector embedding layer and fuse them with lexical and location feature vectors; secondly, we build a multichannel layer based on CNN and BiLSTM to extract local and global features of the microbial text; then we build an attention mechanism layer to extract the important semantic features of the microbial text; thirdly, we merge the multichannel output in the fusion layer and use soft; finally, the results are merged in the fusion layer, and a surtax function is used in the output layer for sentiment classification. The results show that the F1 value of the MCMF-A sentiment analysis model reaches 90.21%, which is 9.71% and 9.14% higher than the benchmark CNN and BiLSTM models, respectively. The constructed dataset is small in size, and the multimodal information such as images and speech has not been considered.

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