Have a personal or library account? Click to login
An efficient sentiment analysis using topic model based optimized recurrent neural network Cover

An efficient sentiment analysis using topic model based optimized recurrent neural network

Open Access
|Jun 2021

Figures & Tables

Figure 1:

The flow of the proposed algorithm.

Figure 2:

Flow of the proposed HCT algorithm.

Figure 3:

Hotel dataset frequent terms.

Figure 4:

Movie dataset frequent terms.

Figure 5:

Mobile dataset frequent terms.

Figure 6:

Accuracy comparison of the proposed model for three different datasets.

Figure 7:

Performance of single-layer Bi-LSTM on Amazon dataset.

Figure 8:

Performance of single-layer Bi-LSTM on Yelp dataset.

Figure 9:

Performance of single-layer Bi-LSTM on IMDB dataset.

Figure 10:

Performance of HCT two-layer Bi-LSTM on Amazon dataset.

Figure 11:

Performance of HCT two-layer Bi-LSTM on Yelp dataset.

Figure 12:

Performance of HCT two-layer Bi-LSTM on IMDB dataset.

Dataset statistics_

Dataset domainTotal+ve‒ve
Restaurant from Yelp1,000500500
Mobile from Amazon1,000500500
Movies from IMDB1,000500500

Comparison of proposed HCL-Bi-LSTM model_

ModelSingle-layer Bi-LSTM (Hameed and Garcia-Zapirain, 2020)Two-layer Bi-LSTMTwo-layer HCT Bi-LSTM
DatasetTVTVTV
Amazon0.830.510.910.700.950.76
Yelp0.840.700.850.720.860.75
IMDB0.710.810.900.810.950.82

Various model parameters_

ParameterValue
Vocabulary size10,000
Bi-LSTM2 layer
Dense1
ActivationSigmoid
OptimizerAdam function
Loss FunctionBinary cross-entropy
Input Length100
Learning rate0.002
Epoch10
Language: English
Page range: 1 - 12
Submitted on: Feb 21, 2021
|
Published on: Jun 22, 2021
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2021 Nikhlesh Pathik, Pragya Shukla, published by Professor Subhas Chandra Mukhopadhyay
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.