<abstract xmlns="http://www.w3.org/1999/xhtml"><p>In recent years, topic modeling and deep neural network-based methods have attracted much attention in sentiment analysis of online reviews. This paper presents a hybrid topic model-based approach for aspect extraction and sentiment classification of textual reviews. Latent Dirichlet allocation applied for aspect extraction and two-layer bi-directional long short-term memory (LSTM) for sentiment classification. This work also proposes a hill climbing-based approach for tunning model hyperparameters. The proposed model evaluated on three different datasets. Compared to the single-layer Bi-LSTM model, the proposed model gives 95, 95, and 86% accuracy for the movie, mobile, and hotel domain, respectively.</p></abstract>

In recent years, topic modeling and deep neural network-based methods have attracted much attention in sentiment analysis of online reviews. This paper presents a hybrid topic model-based approach for aspect extraction and sentiment classification of textual reviews. Latent Dirichlet allocation applied for aspect extraction and two-layer bi-directional long short-term memory (LSTM) for sentiment classification. This work also proposes a hill climbing-based approach for tunning model hyperparameters. The proposed model evaluated on three different datasets. Compared to the single-layer Bi-LSTM model, the proposed model gives 95, 95, and 86% accuracy for the movie, mobile, and hotel domain, respectively.

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

International Journal on Smart Sensing and Intelligent Systems

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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An efficient sentiment analysis using topic model based optimized recurrent neural network: Pathik and Shukla

In recent years, topic modeling and deep neural network-based methods have attracted much attention in sentiment analysis of online reviews. This paper...

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

Online veröffentlicht: 22. Juni 2021

Seitenbereich: 1 - 12

Eingereicht: 21. Feb. 2021

DOI: https://doi.org/10.21307/ijssis-2021-011

Schlüsselwörter
Bi-LSTM, LDA, Hill-climbing, Classification, Hyperparameters

© 2021 Nikhlesh Pathik et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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

Online veröffentlicht: 22. Juni 2021

Seitenbereich: 1 - 12

Eingereicht: 21. Feb. 2021

DOI: https://doi.org/10.21307/ijssis-2021-011

SchlüsselwörterBi-LSTM, LDA, Hill-climbing, Classification, Hyperparameters

© 2021 Nikhlesh Pathik et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
Bi-LSTM, LDA, Hill-climbing, Classification, Hyperparameters