ItemArticle

Decoding customer sentiments in quick commerce: comparative insights from BlinkIt, Zepto, and JioMart utilizing machine and deep learning models

Title
Decoding customer sentiments in quick commerce: comparative insights from BlinkIt, Zepto, and JioMart utilizing machine and deep learning models
Creator
Basha, Md Shaik Amzad; Gopal, Merugu Venu; Sucharitha, M. Martha
Description
The rapid expansion of quick commerce platforms like BlinkIt, Zepto, and JioMart has introduced unique challenges in understanding customer sentiments due to their operational focus on ultra-fast deliveries and hyper-local logistics. This study conducts a comprehensive analysis of sentiment classification methodologies, exploring both traditional ML techniques and advanced DL models to classify customer reviews into positive, negative, and neutral categories. Traditional models, while offering simplicity and interpretability, achieved moderate accuracy (83% with SVM) but struggled to capture the complexities of neutral sentiments. In contrast, DL models, particularly LSTM, achieved superior performance with an accuracy of 88.96% and a macro F1-score of 0.64, leveraging pre-trained embeddings like GloVe to enhance semantic understanding and contextual representation. Further experiments with optimizers, including Adam, RMSprop, SGD, and Nadam, revealed their limited impact on resolving class imbalance and improving neutral sentiment classification. To address these challenges, we integrated hybrid architectures combining GloVe and BERT embeddings, achieving a significant accuracy of 90.69% and demonstrating improved generalization across sentiment classes. However, the classification of neutral sentiments remained a persistent challenge, underscoring the need for advanced techniques like data augmentation and ensemble strategies. This research highlights the importance of adopting hybrid and deep learning-based approaches for sentiment analysis in quick commerce platforms. The findings provide actionable insights for enhancing customer satisfaction and service quality, while also paving the way for future research in domain-specific sentiment classification and scalable solutions for underrepresented sentiment categories. The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2026.
Source
International Journal of System Assurance Engineering and Management;
Date
01-01-2026
Publisher
Springer
Subject
Deep learning; Hybrid models; Multilingual NLP; Quick commerce; Sentiment analysis
Coverage
Basha M.S.A., GITAM School of Business, Gandhi Institute of Technology and Management (Deemed to be University), Bengaluru,, India, Christ Academy Institute for Advanced Studies, Bengaluru, India; Gopal M.V., GITAM School of Business, Gandhi Institute of Technology and Management (Deemed to be University), Hyderabad, India; Sucharitha M.M., Christ (Deemed to be University), Bengaluru, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISSN: 9756809;
Format
online
Language
English
Type
Article

Citation

Basha, Md Shaik Amzad; Gopal, Merugu Venu; Sucharitha, M. Martha, “Decoding customer sentiments in quick commerce: comparative insights from BlinkIt, Zepto, and JioMart utilizing machine and deep learning models,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 19, 2026, https://archives.christuniversity.in/items/show/22033.

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