Open Access
Research Article
Optimized multi-class sentiment classification of Flipkart product reviews using deep learning
*Sonica UpadhyayCorresponding authors40847819@gmail.comDepartment of Computer Science Banasthali VidyapithNewai, Rajasthan, 304022, IndiaView full profile → , Yogesh Kumar Guptagyogesh@banasthali.inDepartment of Computer Science Banasthali VidyapithNewai, Rajasthan, 304022, IndiaView full profile →
* Corresponding author · click or hover a name for details
- Received:
- 01 Apr 2025
- Published Online:
- 01 May 2026
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIOS-2291
- Pages:
- 2003–2010
Abstract
Due to the exponential growth of textual information on the internet, online monitoring and mining of textual data have become a prominent task for researchers, which requires a deeper understanding of text classification algorithms. Several machines and deep learning algorithms have performed well in natural language processing for textual classification. These classification algorithms extract helpful information from textual resources and automatically classify them into multiple predefined categories based on their content and subject matter. In this research paper, a comparative review sentiments analysis of flip kart products has been done by using different deep learning algorithms like Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), Bidirectional Encoder Representations from Transformers (BERT) and Robustly Optimized BERT Approach (RoBERTa) on a given dataset in which their efficiency is analysed and compared. and from obtained experimental RoBERTa model scored highest prediction accuracy, with 86.94%.
Keywords
Subject Classifications
Primary 68T01Secondary 97C30
References
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