Abstract: In an era marked by a proliferation of online reviews across various domains, navigating the extensive and diverse range of opinions can be challenging. Sentiment analysis aims to extract and interpret sentiments from these vast pools of data using computational linguistics and information retrieval techniques. This study focuses on employing deep learning methods such as Deep Belief Networks (DBN) and Gated Recurrent Units (GRU) to classify reviews into positive and negative sentiments, addressing the issue of information overload in Product Reviews. The primary objective is to develop an efficient sentiment analysis system that reliably categorizes reviews as positive or negative. The study introduces a novel sentiment analysis framework combining Deep Belief Networks and Gated Recurrent Units for online product review classification, enhancing accuracy through advanced feature extraction and classification techniques. The comprehensive preparation pipeline—comprising data splitting, stemming, stop word removal and special character separation—enhances dataset refinement for improved classification accuracy. The proposed framework consists of four main phases: pre-processing, feature extraction, classification, and evaluation. During the preparation phase, the dataset is meticulously cleaned and refined to reduce noise and enhance signal quality. Significant features are then extracted from the pre-processed data using advanced feature extraction algorithms. The DBN-GRU model leverages these features for sentiment classification, effectively distinguishing between positive and negative attitudes. The framework’s performance is subsequently evaluated to assess its efficacy in accurately classifying reviews. The combination of in-depth pre-processing procedures and the DBN-GRU technique yielded promising results in sentiment categorization. The framework demonstrated a high accuracy of 98.74% in differentiating between positive and negative sentiments, thereby facilitating the effective analysis of online reviews. This study presents a robust framework for sentiment analysis, utilizing the DBN-GRU method to classify online reviews. Through extensive preprocessing and advanced classification techniques, the system addresses the challenges of noise and information overload in online reviews, providing valuable insights for both consumers and businesses.

Shaista Khan, J Chandra Sekhar, J. Ramu, Yousef A.Baker El-Ebiary, K.Aanandha Saravanan, Kuchipudi Prasanth Kumar and Prajakta Uday Waghe. “A Hybrid DBN-GRU Model for Enhanced Sentiment Analysis in Product Reviews”. International Journal of Advanced Computer Science and Applications (IJACSA) 15.7 (2024). http://dx.doi.org/10.14569/IJACSA.2024.0150789

@article{Khan2024,
title = {A Hybrid DBN-GRU Model for Enhanced Sentiment Analysis in Product Reviews},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150789},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150789},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {7},
author = {Shaista Khan and J Chandra Sekhar and J. Ramu and Yousef A.Baker El-Ebiary and K.Aanandha Saravanan and Kuchipudi Prasanth Kumar and Prajakta Uday Waghe}
}