A modernized approach to sentiment analysis of product reviews using BiGRU and RNN based LSTM deep learning models
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Le résumé fourni par la source
With the advent of Web 2.0 and popularization of online shopping applications, there has been a huge upsurge of user generated content in recent times. Leading companies and top brands are trying to exploit this data and analyze the market demands and reach of their products among consumers using opinion mining. Sentiment analysis is a hot topic of research in the e-commerce industry. This paper proposes such a novel sentence level sentiment analysis approach for mining online product reviews using natural language processing and deep learning techniques. The proposed model consists of various stages like web crawling and collecting product reviews, preprocessing, feature extraction, sentiment analysis and polarity classification. The input reviews are preprocessed using natural language processing techniques like tokenization, lemmatization, stop word removal, named entity recognition and part of speech tagging. Feature extraction is done using bidirectional gated recurrent unit shortly called as BiGRU feature extractor and the sentiments are classified into three polarities such as positive, negative and neutral using a hybrid recurrent neural network based long short-term memory classifier. The specific combination of techniques employed here and applying it to a new kind of online product review is making the proposed model to be novel. Performance evaluation metrics such as accuracy, precision, recall, F measure and AUC are calculated for the proposed model and compared with many existing techniques like deep convolutional neural network, multilayer perceptron, CapsuleNet and generative adversarial networks. The proposed model can be used in a variety of applications like market research, social network mining, recommendation systems, brand analysis, product quality management etc. and is found to generate promising results when compared to prevailing models.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- A modernized approach to sentiment analysis of product reviews using BiGRU and RNN based LSTM deep learning models
- Date Crossref
- 13/05/2025
- Éditeur
- Springer Science and Business Media LLC
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Bharath University pays non établi dans la noticeUniversité ou école supérieure
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Amity University Madhya Pradesh ASET-CSE pays non établi dans la noticeUniversité ou école supérieure
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Shiv Nadar University pays non établi dans la noticeUniversité ou école supérieure
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Chitkara University Centre for Research Impact & Outcome pays non établi dans la noticeUniversité ou école supérieure
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Leeds Beckett University pays non établi dans la noticeUniversité ou école supérieure
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King Saud University Department of Self-Development Skill pays non établi dans la noticeUniversité ou école supérieure
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Bharath Institute of Higher Education and Research Department of Computer Science and Engineering pays non établi dans la noticeStructure de recherche
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Chennai Institute of Technology Department of Computer Science and Engineering pays non établi dans la noticeStructure de recherche
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Kebri Dehar University Department of Computer Science Éthiopie (pays nommé en fin d’affiliation)Université ou école supérieure
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School of Built Environment pays non établi dans la noticeUniversité ou école supérieure
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College of Computer and Information Sciences Computer Science Department pays non établi dans la noticeUniversité ou école supérieure
Bharath University, ASET-CSE — Amity University Madhya Pradesh et Shiv Nadar University, avec 8 autres affiliations. Pays d’affiliation : Éthiopie.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.