Alam, Md. Golam RabiulHossain, Md. Zahid2026-08-162026-08-1620262026-03ID 24366023https://hdl.handle.net/10361/29126This thesis is submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science and Engineering, 2026.Cataloged from PDF version of thesis.Includes bibliographical references (pages 46-50).Ordinal sentiment classification from customer reviews using star rating labels is challenging due to subtle distinctions between adjacent rating levels, particularly in low-resource and multilingual settings. Existing benchmarks are predominantly English-centric and text-only, limiting their ability to reflect real-world e-commerce scenarios. We introduce DarazMM, a large-scale Bangla-centric multilingual multimodal dataset for five-class star rating prediction, covering Bangla, English, codemixed Bangla-English and Romanized Bangla. The dataset is released in two settings: a text-only corpus of 257,817 reviews and a multimodal subset of 25,452 reviews that includes review text, customer-uploaded images, advertised product images and corresponding product descriptions. We benchmark zero-shot, few-shot and supervised models, and propose two neuro-symbolic frameworks that model rating ordinality and cross-modal interactions. Our best supervised framework achieves 0.87 accuracy and 0.83 macro-F1, while the best zero-shot and few-shot models reach 0.71 and 0.79 accuracy, respectively. The dataset will be made publicly available.65 pagesen-USAttribution-NonCommercial-NoDerivatives 4.0 InternationalBRAC University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.http://creativecommons.org/licenses/by-nc-nd/4.0/Sentiment analysisNatural language processingNeuro-symbolic AIMultimodal dataseMultilingual datasetE-commerceDarazMMBengali languageBenchmark evaluationProduct reviewsInformation retrievalNatural language processing (Computer science).Electronic commerce.Electronic data processing.Consumer goods--Evaluation.Thesis