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

<p>Computational Intelligence (CI) is a sub-field of Artificial Intelligence (AI) that concentrates on the development of intelligent techniques and models to solve complex problems. When it comes to data analytics, CI techniques can be highly effective in handling large volumes of data, extracting meaningful patterns, and making accurate predictions. An increasing number of online reviews were being posted on the Internet every day with the rapid growth of Electronic Commerce (E-Commerce) and social networks. In the fastest growing research area, Sentiment Analysis (SA) is one among them that helps consumers in making better decisions relating to purchases through proper analysis and understanding of shared sentiments from social media and the web. In recent times, several approaches were modelled for acquiring insights from such datasets. But still, there comes a problem in managing text of large size; hence, precise polarity recognition of consumer reviews was an exciting and ongoing issue. This article introduces a novel Sentiment Analysis on Product Reviews using Enhanced Grasshopper Optimization with Deep Learning (SAPR-EGODL) approach. The objective of the SAPR-EGODL approach lies in the identification and classification of different sentiment types that exist in product reviews. At the initial stage, data preprocessing takes place which transforms the product review data into meaningful data. Next, the SAPREGODL technique employs Multi-Head Attention-based Bidirectional Long Short Term (MHABLSTM) technique for sentiment classification. In this study, the EGO model is exploited for improving the classification accomplishment of the MHABLSTM technique. A sequence of simulations was accomplished on different datasets for examining the advanced sentiment classification results of the SAPR-EGODL technique. The comprehensive relative research exhibited the promising accomplishment of the SAPR-EGODL model compared to current techniques.</p>
</abstract>

Computational Intelligence (CI) is a sub-field of Artificial Intelligence (AI) that concentrates on the development of intelligent techniques and models to solve complex problems. When it comes to data analytics, CI techniques can be highly effective in handling large volumes of data, extracting meaningful patterns, and making accurate predictions. An increasing number of online reviews were being posted on the Internet every day with the rapid growth of Electronic Commerce (E-Commerce) and social networks. In the fastest growing research area, Sentiment Analysis (SA) is one among them that helps consumers in making better decisions relating to purchases through proper analysis and understanding of shared sentiments from social media and the web. In recent times, several approaches were modelled for acquiring insights from such datasets. But still, there comes a problem in managing text of large size; hence, precise polarity recognition of consumer reviews was an exciting and ongoing issue. This article introduces a novel Sentiment Analysis on Product Reviews using Enhanced Grasshopper Optimization with Deep Learning (SAPR-EGODL) approach. The objective of the SAPR-EGODL approach lies in the identification and classification of different sentiment types that exist in product reviews. At the initial stage, data preprocessing takes place which transforms the product review data into meaningful data. Next, the SAPREGODL technique employs Multi-Head Attention-based Bidirectional Long Short Term (MHABLSTM) technique for sentiment classification. In this study, the EGO model is exploited for improving the classification accomplishment of the MHABLSTM technique. A sequence of simulations was accomplished on different datasets for examining the advanced sentiment classification results of the SAPR-EGODL technique. The comprehensive relative research exhibited the promising accomplishment of the SAPR-EGODL model compared to current techniques.

Computational Intelligence-based Data Analytics for Sentiment Classification on Product Reviews

Computer Engineering, Artificial Intelligence and Robotics department

Journal of Smart Internet of Things

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

Computational Intelligence (CI) is a sub-field of Artificial Intelligence (AI) that concentrates on the development of intelligent techniques and models to...

Computational Intelligence-based Data Analytics for Sentiment Classification on Product Reviews

Online veröffentlicht: 15. Dez. 2023

Seitenbereich: 84 - 104

Eingereicht: 17. Aug. 2023

Akzeptiert: 13. Sept. 2023

DOI: https://doi.org/10.2478/jsiot-2023-0012

Schlüsselwörter
Data analytics, Sentiment analysis, Product reviews, Product/Service development, Deep learning, Enhanced grasshopper optimization algorithm

© 2023 Ramy Riad Al-Fatlawy, published by Sciendo

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

Computational Intelligence-based Data Analytics for Sentiment Classification on Product Reviews

Online veröffentlicht: 15. Dez. 2023

Seitenbereich: 84 - 104

Eingereicht: 17. Aug. 2023

Akzeptiert: 13. Sept. 2023

DOI: https://doi.org/10.2478/jsiot-2023-0012

SchlüsselwörterData analytics, Sentiment analysis, Product reviews, Product/Service development, Deep learning, Enhanced grasshopper optimization algorithm

© 2023 Ramy Riad Al-Fatlawy, published by Sciendo

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

Schlüsselwörter
Data analytics, Sentiment analysis, Product reviews, Product/Service development, Deep learning, Enhanced grasshopper optimization algorithm