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Using BiLSTM Structure with Cascaded Attention Fusion Model for Sentiment Analysis

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Title Using BiLSTM Structure with Cascaded Attention Fusion Model for Sentiment Analysis
 
Creator Sangeetha, J
Kumaran, U
 
Subject CAFM
Deep learning
Deep neural network
Long short-term memory
Natural language processing
 
Description 444-449
In the last decade, sentiment analysis has been a popular research area in the domains of natural language processing and
data mining. Sentiment analysis has several commercial and social applications. The technique is essential to analyse the
customer experience to develop customer loyalty and maintenance through better assistance. Deep Neural Network (DNN)
models have recently been used to do sentiment analysis tasks with promising results. The disadvantage of such models is
that they value all characteristics equally. We propose a Cascaded Attention Fusion Model-based BiLSTM to address these
issues (CAFM-BiLSTM). Multiple heads with embedding and BiLSTM layers are concatenated in the proposed CAFMBiLSTM.
The information from both deep multi-layers is merged and provided as input to the BiLSTM layer later in this
paper. The results of our fusion model are superior to those of the existing models. Our model outperforms the competition
for lengthier sentence sequences and pays special attention to referral words. The accuracy of the proposed CAFM-BiLSTM
is 5.1%, 5.25%, 6.1%, 12.2%, and 13.7% better than RNN-LSTM, SVM, NB, RF and DT respectively.
 
Date 2023-04-03T09:54:29Z
2023-04-03T09:54:29Z
2023-04
 
Type Article
 
Identifier 0022-4456 (Print); 0975-1084 (Online)
http://nopr.niscpr.res.in/handle/123456789/61652
https://doi.org/10.56042/jsir.v82i04.72385
 
Language en
 
Publisher NIScPR-CSIR,India
 
Source JSIR Vol.82(04) [April 2023]