Adaptive Particle Grey Wolf Optimizer with Deep Learning-based Sentiment Analysis on Online Product Reviews
The increasing use of e-commerce websites and social networks is continually generating an immense amount of data in various forms, such as text, images or sounds, videos, etc. Sentiment analysis (SA) in online product reviews is a method of identifying the overall sentiment of customers about a spe...
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| Format: | Article |
| Language: | English |
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D. G. Pylarinos
2023-06-01
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| Series: | Engineering, Technology & Applied Science Research |
| Subjects: | |
| Online Access: | https://etasr.com/index.php/ETASR/article/view/5787 |
| _version_ | 1797696131322347520 |
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| author | Durai Elangovan Varatharaj Subedha |
| author_facet | Durai Elangovan Varatharaj Subedha |
| author_sort | Durai Elangovan |
| collection | DOAJ |
| description | The increasing use of e-commerce websites and social networks is continually generating an immense amount of data in various forms, such as text, images or sounds, videos, etc. Sentiment analysis (SA) in online product reviews is a method of identifying the overall sentiment of customers about a specific product or service. This study used Natural Language Processing (NLP) and Machine Learning (ML) algorithms to identify and extract opinions and emotions expressed in text. Online reviews are often written in informal language, slang, and dialects, making it difficult for ML models to accurately classify sentiments. In addition, the use of misspelled words or incorrect grammar can further complicate the analysis. The recent developments of Deep Learning (DL) models can be used for the accurate classification of sentiments. This paper presents an Adaptive Particle Grey Wolf Optimizer with Deep Learning Based Sentiment Analysis (APGWO-DLSA) method to accurately classify sentiments in product reviews. Initially, data pre-processing was performed to improve the quality of the product reviews using the word2vec embedding process. For sentiment classification, the proposed method used a Deep Belief Network (DBN) model. Finally, the hyperparameter tuning of the DBN was performed using the APGWO algorithm. An extensive experimental analysis demonstrated the improved results of APGWO-DLSA over other methods, showing a maximum accuracy of 94.77% and 85.31% on the Cell Phones And Accessories (CPAA) and Amazon Products (AP) datasets.
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| first_indexed | 2024-03-12T03:21:52Z |
| format | Article |
| id | doaj.art-7b4ce1732e3c499ba7dd3b946a584566 |
| institution | Directory Open Access Journal |
| issn | 2241-4487 1792-8036 |
| language | English |
| last_indexed | 2024-03-12T03:21:52Z |
| publishDate | 2023-06-01 |
| publisher | D. G. Pylarinos |
| record_format | Article |
| series | Engineering, Technology & Applied Science Research |
| spelling | doaj.art-7b4ce1732e3c499ba7dd3b946a5845662023-09-03T13:55:56ZengD. G. PylarinosEngineering, Technology & Applied Science Research2241-44871792-80362023-06-0113310.48084/etasr.5787Adaptive Particle Grey Wolf Optimizer with Deep Learning-based Sentiment Analysis on Online Product ReviewsDurai Elangovan0Varatharaj Subedha1Sathyabama Institute of Science and Technology, IndiaDepartment of Computer Science and Engineering, Panimalar Institute of Technology, IndiaThe increasing use of e-commerce websites and social networks is continually generating an immense amount of data in various forms, such as text, images or sounds, videos, etc. Sentiment analysis (SA) in online product reviews is a method of identifying the overall sentiment of customers about a specific product or service. This study used Natural Language Processing (NLP) and Machine Learning (ML) algorithms to identify and extract opinions and emotions expressed in text. Online reviews are often written in informal language, slang, and dialects, making it difficult for ML models to accurately classify sentiments. In addition, the use of misspelled words or incorrect grammar can further complicate the analysis. The recent developments of Deep Learning (DL) models can be used for the accurate classification of sentiments. This paper presents an Adaptive Particle Grey Wolf Optimizer with Deep Learning Based Sentiment Analysis (APGWO-DLSA) method to accurately classify sentiments in product reviews. Initially, data pre-processing was performed to improve the quality of the product reviews using the word2vec embedding process. For sentiment classification, the proposed method used a Deep Belief Network (DBN) model. Finally, the hyperparameter tuning of the DBN was performed using the APGWO algorithm. An extensive experimental analysis demonstrated the improved results of APGWO-DLSA over other methods, showing a maximum accuracy of 94.77% and 85.31% on the Cell Phones And Accessories (CPAA) and Amazon Products (AP) datasets. https://etasr.com/index.php/ETASR/article/view/5787sentiment analysisonline product reviewsmachine learningdeep learningnatural language processing |
| spellingShingle | Durai Elangovan Varatharaj Subedha Adaptive Particle Grey Wolf Optimizer with Deep Learning-based Sentiment Analysis on Online Product Reviews Engineering, Technology & Applied Science Research sentiment analysis online product reviews machine learning deep learning natural language processing |
| title | Adaptive Particle Grey Wolf Optimizer with Deep Learning-based Sentiment Analysis on Online Product Reviews |
| title_full | Adaptive Particle Grey Wolf Optimizer with Deep Learning-based Sentiment Analysis on Online Product Reviews |
| title_fullStr | Adaptive Particle Grey Wolf Optimizer with Deep Learning-based Sentiment Analysis on Online Product Reviews |
| title_full_unstemmed | Adaptive Particle Grey Wolf Optimizer with Deep Learning-based Sentiment Analysis on Online Product Reviews |
| title_short | Adaptive Particle Grey Wolf Optimizer with Deep Learning-based Sentiment Analysis on Online Product Reviews |
| title_sort | adaptive particle grey wolf optimizer with deep learning based sentiment analysis on online product reviews |
| topic | sentiment analysis online product reviews machine learning deep learning natural language processing |
| url | https://etasr.com/index.php/ETASR/article/view/5787 |
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