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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Issue: February 2026
Volume 11 | Issue 2
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Impact Factor : 8.14
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Volume Published : 11
Issue Published : 117
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| Paper Title: | Sentiment Analysis of Flipkart Product Reviews using Machine Learning, Deep Learning and Large Language Models |
| Authors Name: | Sumedha Arya , Nirmal Gaud |
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IJRTI_209020
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| Published Paper Id: | IJRTI2601017 |
| Published In: | Volume 11 Issue 1, January-2026 |
| DOI: | |
| Abstract: | This study focuses on sentiment analysis of Flipkart product reviews using machine learning (ML), deep learning (DL), and transformer-based large language models (LLMs). The objective is to classify reviews into positive, negative, or neutral categories. In the literature, we highlighted key challenges in the field, including limited dataset diversity, class imbalance, and insufficient feature extraction techniques. To address these, various models based on ML, DL, and LLMs were implemented and evaluated using standard metrics such as accuracy, precision, recall, and F1-score. Among the models, RoBERTa demonstrated the highest performance (accuracy: 0.9452, F1-score: 0.95), followed closely by CNN-LSTM (accuracy: 0.9454, F1-score: 0.9466) and LSTM (accuracy: 0.9482, F1-score: 0.9482). Among traditional ML models, Random Forest achieved the best results (accuracy and F1-score: 0.9173). The findings suggest that RoBERTa is well-suited for applications requiring high precision, while CNN-LSTM offers a robust alternative in resource-constrained environments. Future research will focus on hyperparameter tuning, and multimodal data analysis. |
| Keywords: | Sentiment Analysis, Flipkart Reviews, Machine Learning, Deep Learning, Transformer Models, RoBERTa, CNN-LSTM |
| Cite Article: | "Sentiment Analysis of Flipkart Product Reviews using Machine Learning, Deep Learning and Large Language Models", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.11, Issue 1, page no.a112-a117, January-2026, Available :http://www.ijrti.org/papers/IJRTI2601017.pdf |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2601017
Registration ID:209020
Published In: Volume 11 Issue 1, January-2026
DOI (Digital Object Identifier):
Page No: a112-a117 Country: New Delhi, Delhi, India Research Area: Other Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2601017 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2601017 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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