TDO-Spider Taylor ChOA: An Optimized Deep-Learning-Based Sentiment Classification and Review Rating Prediction
Modern review websites, namely Yelp and Amazon, permit the users to post online reviews for numerous businesses, services and products. Currently, online reviewing is an imperative task in the manipulation of shopping decisions produced by customers. These reviews afford consumers experience and inf...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/20/10292 |
| _version_ | 1797475650191228928 |
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| author | Santosh Kumar Banbhrani Bo Xu Pir Dino Soomro Deepak Kumar Jain Hongfei Lin |
| author_facet | Santosh Kumar Banbhrani Bo Xu Pir Dino Soomro Deepak Kumar Jain Hongfei Lin |
| author_sort | Santosh Kumar Banbhrani |
| collection | DOAJ |
| description | Modern review websites, namely Yelp and Amazon, permit the users to post online reviews for numerous businesses, services and products. Currently, online reviewing is an imperative task in the manipulation of shopping decisions produced by customers. These reviews afford consumers experience and information regarding the superiority of the product. The prevalent method of strengthening online review evolution is the performance of Sentiment Classification, which is an attractive domain in industrial and academic research. The review helps various domains, and it is problematic to collect interpreted training data. In this paper, an effectual Review Rating Prediction and Sentiment Classification was developed. Here, a Gated Recurrent Unit (GRU) was employed for the Sentiment Classification process, whereas a Hierarchical Attention Network (HAN) was applied for Review Rating Prediction. The significant features, such as statistical, SentiWordNet and classification features, were extracted for the Sentiment Classification and Review Rating Prediction process. Moreover, the GRU was trained by the designed TD-Spider Taylor ChOA approach, and the HAN was trained by the designed Jaya-TDO approach. The experimental results show that the proposed Jaya-TDO technique attained a better performance of 0.9425, 0.9654 and 0.9538, and that TD-Spider Taylor ChOA achieved 0.9524, 0.9698 and 0.9588 in terms of the precision, recall and F-measure. |
| first_indexed | 2024-03-09T20:48:04Z |
| format | Article |
| id | doaj.art-daedd828dcd24e67a401b769e8918f78 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T20:48:04Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-daedd828dcd24e67a401b769e8918f782023-11-23T22:42:15ZengMDPI AGApplied Sciences2076-34172022-10-0112201029210.3390/app122010292TDO-Spider Taylor ChOA: An Optimized Deep-Learning-Based Sentiment Classification and Review Rating PredictionSantosh Kumar Banbhrani0Bo Xu1Pir Dino Soomro2Deepak Kumar Jain3Hongfei Lin4School of Computer Science and Technology, Dalian University of Technology, Ganjingzi District, Dalian 116024, ChinaSchool of Computer Science and Technology, Dalian University of Technology, Ganjingzi District, Dalian 116024, ChinaSchool of Computer Science and Technology, Dalian Maritime University, No. 1, Linggong Road, Dalian 116026, ChinaKey Laboratory of Intelligent Air Ground Cooperative Control for Universities in Chongqing, College of Automation, Chongqing University of Posts and Telecommunications, No. 2, Chongwen Road, Nan’an District, Chongqing 400065, ChinaSchool of Computer Science and Technology, Dalian University of Technology, Ganjingzi District, Dalian 116024, ChinaModern review websites, namely Yelp and Amazon, permit the users to post online reviews for numerous businesses, services and products. Currently, online reviewing is an imperative task in the manipulation of shopping decisions produced by customers. These reviews afford consumers experience and information regarding the superiority of the product. The prevalent method of strengthening online review evolution is the performance of Sentiment Classification, which is an attractive domain in industrial and academic research. The review helps various domains, and it is problematic to collect interpreted training data. In this paper, an effectual Review Rating Prediction and Sentiment Classification was developed. Here, a Gated Recurrent Unit (GRU) was employed for the Sentiment Classification process, whereas a Hierarchical Attention Network (HAN) was applied for Review Rating Prediction. The significant features, such as statistical, SentiWordNet and classification features, were extracted for the Sentiment Classification and Review Rating Prediction process. Moreover, the GRU was trained by the designed TD-Spider Taylor ChOA approach, and the HAN was trained by the designed Jaya-TDO approach. The experimental results show that the proposed Jaya-TDO technique attained a better performance of 0.9425, 0.9654 and 0.9538, and that TD-Spider Taylor ChOA achieved 0.9524, 0.9698 and 0.9588 in terms of the precision, recall and F-measure.https://www.mdpi.com/2076-3417/12/20/10292gated recurrent unithierarchical attention networknatural language processingReview Rating PredictionSentiment ClassificationTasmanian Devil Optimization |
| spellingShingle | Santosh Kumar Banbhrani Bo Xu Pir Dino Soomro Deepak Kumar Jain Hongfei Lin TDO-Spider Taylor ChOA: An Optimized Deep-Learning-Based Sentiment Classification and Review Rating Prediction Applied Sciences gated recurrent unit hierarchical attention network natural language processing Review Rating Prediction Sentiment Classification Tasmanian Devil Optimization |
| title | TDO-Spider Taylor ChOA: An Optimized Deep-Learning-Based Sentiment Classification and Review Rating Prediction |
| title_full | TDO-Spider Taylor ChOA: An Optimized Deep-Learning-Based Sentiment Classification and Review Rating Prediction |
| title_fullStr | TDO-Spider Taylor ChOA: An Optimized Deep-Learning-Based Sentiment Classification and Review Rating Prediction |
| title_full_unstemmed | TDO-Spider Taylor ChOA: An Optimized Deep-Learning-Based Sentiment Classification and Review Rating Prediction |
| title_short | TDO-Spider Taylor ChOA: An Optimized Deep-Learning-Based Sentiment Classification and Review Rating Prediction |
| title_sort | tdo spider taylor choa an optimized deep learning based sentiment classification and review rating prediction |
| topic | gated recurrent unit hierarchical attention network natural language processing Review Rating Prediction Sentiment Classification Tasmanian Devil Optimization |
| url | https://www.mdpi.com/2076-3417/12/20/10292 |
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