Skoči na glavni sadržaj

International journal of electrical and computer engineering systems, Vol. 17 No. 1, 2026.

Izvorni znanstveni članak

https://doi.org/10.32985/ijeces.17.1.1

Leveraging Word2Vec-Enhanced CNN-LSTM Hybrid Architecture for Sentiment Analysis in E-Commerce Product Reviews

Kosala Natarajan ; Department of Computer Science and Engineering Sathyabama Institute of Science and Technology, Jeppiar Nagar, Chennai, Tamil Nadu - 600119, India *
Nirmalrani V ; Department of Computer Science and Engineering Sathyabama Institute of Science and Technology, Jeppiar Nagar, Chennai, Tamil Nadu - 600119, India
Gowri S ; Department of Computer Science and Engineering Sathyabama Institute of Science and Technology, Jeppiar Nagar, Chennai, Tamil Nadu - 600119, India.
Ramya G Franklin ; Department of Computer Science and Engineering Sathyabama Institute of Science and Technology, Jeppiar Nagar, Chennai, Tamil Nadu - 600119, India.
Poornima D ; Department of Computer Science and Engineering Sathyabama Institute of Science and Technology, Jeppiar Nagar, Chennai, Tamil Nadu - 600119, India.
Jabez J ; Department of Computer Science and Engineering Sathyabama Institute of Science and Technology, Jeppiar Nagar, Chennai, Tamil Nadu - 600119, India.

* Dopisni autor.

Puni tekst: engleski pdf 1.316 Kb

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preuzimanja: 112

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Sažetak

The amalgamation of machine learning (ML) techniques and natural language processing (NLP) is leveraged to evaluate the sentiment of textual input. With the increasing popularity of e-commerce platforms like Amazon, product reviews have emerged as an essential source of information for potential purchasers, providing insights into product quality and performance from the consumers' viewpoints. This study aims to systematically organize and analyze customer opinions to effectively capture consumer sentiment based on product reviews. In this study, we propose a deep learning framework that combines a stacked 1D convolutional layer (CNN) with a Long Short-Term Memory (LSTM) network, using pre-trained Word2Vec embedding as fixed input representations. Evaluated on a large Amazon product review dataset, our model — StackedCNN-LSTM-W2V — achieves a classification accuracy of 99%, outperforming traditional CNN, LSTM, and logistic regression baselines.

Ključne riječi

Sentiment analysis; Amazon product reviews; StackedCNN-LSTM; Text classification; Deep learning; Word embedding;

Hrčak ID:

342318

URI

https://hrcak.srce.hr/342318

Datum izdavanja:

5.1.2026.

Posjeta: 275 *