A modernized approach to sentiment analysis of product reviews using BiGRU and RNN based LSTM deep learning models
Abstract 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...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-05-01
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| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-01104-0 |
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| Summary: | Abstract 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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| ISSN: | 2045-2322 |