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 Conference Paper Faculty Publications- Conference Papers 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 Ranka, Monica; Malhotra, Amit; Hada, Bhupendra Singh; Jana, Samiran; Maheshwari, Abhishek; Basha, Md Shaik Amzad Title A name given to the resource Contextual Embedding Fusion for Quick-Commerce Reviews: Rating, 5-Class Classification, and Sentiment Analysis Across Classical ML, DL, and Transformer Models Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Proceedings of the 2025 International Conference on Artificial Intelligence and Emerging Technology, Global AI Summit 2025;pp.1197-1202 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/AISummit66170.2025.11411126" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/AISummit66170.2025.11411126</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105035324628?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105035324628?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 Ranka M., Dayananda Sagar College of Arts, Science and Commerce, Bengaluru, India; Malhotra A., Christ (Deemed to be University), School of Commerce, Finance and Accountancy, Ghaziabad, India; Hada B.S., Christ (Deemed to be University), School of Commerce, Finance and Accountancy, Delhi-NCR, India; Jana S., Soil Institute of Business Design, Haryana, India; Maheshwari A., Christ (Deemed to be University), School of Commerce, Finance and Accountancy, Ghaziabad, India; Basha M.S.A., Christ Academy Institute for Advanced Studies, Gitam School of Business (Deemed to be University), Gandhi Institute of Technology and Managemet, Bangalore, India Description An account of the resource Quick-commerce platforms depend on accurate reading of customer feedback to steer pricing and service quality. We study a dataset of delivery-agent reviews, and build two rating predictors and a text-only classifier suite. On the rating regression task (1-5), we benchmark seven models (Ridge, Lasso, ElasticNet, SVR-RBF, Gradient Boosting, Random Forest, XGBoost). The best test performance is achieved by RandomForestRegressor with MAE = 0.527, RMSE = 0.973, and R2 = 0.555; cross-validation places XGBoost and Gradient Boosting close behind. For 5-class rating classification, we compare seven TF-IDF baselines (LogReg, LinearSVC, two SGD variants, RidgeClassifier, ComplementNB, XGBClassifier) and find SGD (hinge) strongest (Accuracy = 0.817, Macro-F1 = 0.389), yet mid-rating classes (2-4) remain difficult. To address label ordinality, we fine-tune DistilBERT with ordinal heads: CORAL and CORN. These achieve Accuracy = 0.837/0.819 and Macro-F1 = 0.439/0.441, improving Macro-F1 by +5-6 points over TF-IDF baselines, mainly by rescuing classes 2-4. We further map regression outputs to 5 classes via optimized cutpoints, raising Macro-F1 from 0.329 (rounding) to 0.380. Paired, fold-wise comparisons using Wilcoxon signed-rank and bootstrap 95% CIs show consistent gains; with 5 folds the discretized p = 0.0625 indicates all folds favor the better model, suggesting statistical significance with more folds. Overall, ordinal transformers and learned thresholds provide measurable, reproducible improvements for short, noisy e-commerce reviews, delivering higher fidelity on mid-range ratings while preserving high accuracy. 2025 IEEE. Subject The topic of the resource Contextual embedding fusion; E-commerce analytics; Quick-commerce; Rating prediction; Sentiment analysis; Transformer models Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833155379-1; Language A language of the resource English Type The nature or genre of the resource Conference paper 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