Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Basha, Md Shaik Amzad; Gopal, Merugu Venu; Sucharitha, M. Martha Title A name given to the resource Decoding customer sentiments in quick commerce: comparative insights from BlinkIt, Zepto, and JioMart utilizing machine and deep learning models Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived International Journal of System Assurance Engineering and Management; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/s13198-025-03136-1" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s13198-025-03136-1</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105028770913?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105028770913?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant 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 Description An account of the resource 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. Subject The topic of the resource Deep learning; Hybrid models; Multilingual NLP; Quick commerce; Sentiment analysis Publisher An entity responsible for making the resource available Springer Relation A related resource ISSN: 9756809; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online