<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In recent times, the user reviews and opinions postedon social-media like Twitter, Facebook, and Google+, and so on have received considerable attention in the domain of sentiment analysis (SA), offering valued feedback to public as well as private organizations. The examination of reviews roles an essential role for enhancing the product and service qualities. Although numerous SA methods are available in the literature, it is still needed to boost the outcome of the SA for understanding the customer feedbacks and thereby enhances the product quality. The presented article proposesan optimum fully connected deep neural network (OFCDNN) based SA, called OFCDNN-SA technique on social networking data. The OFCDNN-SA technique encompasses distinct procedureslike feature extraction, classification, preprocessing, and parameter optimization. Additionally, Glove technique is applied for the transformation of input data into feature vectors. Moreover, salp swarm optimization (SSO) based hyperparameter optimization technique is derived for maximally selecting the hyperparameters utilized in the DL method. Finally, the FCDNN methodis employed for classification purposes. To investigate the supreme achievement of the OFCDNN-SA method, a wide-ranging simulation assessment is performed and the experimental outputs highlighted the betterment over current methods by means of diverse measures.</p>
</abstract>

In recent times, the user reviews and opinions postedon social-media like Twitter, Facebook, and Google+, and so on have received considerable attention in the domain of sentiment analysis (SA), offering valued feedback to public as well as private organizations. The examination of reviews roles an essential role for enhancing the product and service qualities. Although numerous SA methods are available in the literature, it is still needed to boost the outcome of the SA for understanding the customer feedbacks and thereby enhances the product quality. The presented article proposesan optimum fully connected deep neural network (OFCDNN) based SA, called OFCDNN-SA technique on social networking data. The OFCDNN-SA technique encompasses distinct procedureslike feature extraction, classification, preprocessing, and parameter optimization. Additionally, Glove technique is applied for the transformation of input data into feature vectors. Moreover, salp swarm optimization (SSO) based hyperparameter optimization technique is derived for maximally selecting the hyperparameters utilized in the DL method. Finally, the FCDNN methodis employed for classification purposes. To investigate the supreme achievement of the OFCDNN-SA method, a wide-ranging simulation assessment is performed and the experimental outputs highlighted the betterment over current methods by means of diverse measures.

Modeling of Optimal Fully Connected Deep Neural Network based Sentiment Analysis on Social Networking Data

Journal of Smart Internet of Things

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

In recent times, the user reviews and opinions postedon social-media like Twitter, Facebook, and Google+, and so on have received considerable attention in the...

Modeling of Optimal Fully Connected Deep Neural Network based Sentiment Analysis on Social Networking Data

Data publikacji: 15 gru 2022

Zakres stron: 114 - 132

Otrzymano: 29 lip 2022

Przyjęty: 21 paź 2022

DOI: https://doi.org/10.2478/jsiot-2022-0008

Słowa kluczowe
Social Media, Deep Neural Network, Optimization, Natural Language Processing, Sentiment Analysis,

© 2022 Zaid Alsalami, published by Sciendo

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

Modeling of Optimal Fully Connected Deep Neural Network based Sentiment Analysis on Social Networking Data

Data publikacji: 15 gru 2022

Zakres stron: 114 - 132

Otrzymano: 29 lip 2022

Przyjęty: 21 paź 2022

DOI: https://doi.org/10.2478/jsiot-2022-0008

Słowa kluczoweSocial Media, Deep Neural Network, Optimization, Natural Language Processing, Sentiment Analysis,

© 2022 Zaid Alsalami, published by Sciendo

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

Słowa kluczowe
Social Media, Deep Neural Network, Optimization, Natural Language Processing, Sentiment Analysis,