Sentiment Analysis and Opinion Mining
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Peer reviewed
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An aspect-based sentiment analysis tool that uses an XLM-RoBERTa model in a Django web application for code-mixed Amazon product reviews in India and indicates better context comprehension as compared to conventional polarity based systems.
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With the increasing adoption of online shopping sites in India, there is now a huge amount of review generation in Indian code-switched languages like Hinglish, Marathi-English, etc. Conventional sentiment analysis tools face challenges in analyzing such multilingual data with errors in grammatical structures, vocabulary, and context understanding. The paper proposes an aspect-based sentiment analysis tool that uses an XLM-RoBERTa model in a Django web application for code-mixed Amazon product reviews in India. Reviews in the database are dynamically fetched using the Rainforest API with the help of ASIN numbers. This classifier categorizes the general sentiment as being positive, negative, or neutral while simultaneously extracting sentiments with respect to features like battery life, camera quality, price, and performance, etc. Experimentation with met-rics such as precision, recall, and F1-Score indicates better context comprehension as compared to conventional polarity based systems. These aspects allow for better interpretation for consumers, sellers, and researchers alike. Index Terms—Code-Mixed Language, Hinglish, Aspect-Based Sentiment Analysis, XLM-RoBERTa, Multilingual NLP, E-commerce Analytics
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@article{Mehta2026Aspect,
title = {Aspect-Aware Sentiment Analysis of Code-Mixed Amazon Indian Reviews Using Roberta},
author = {Prof. D. V. Mehta Prof. D. V. Mehta and Shriraj Ranaware Shriraj Ranaware and Rutuja Kharat Rutuja Kharat and Karishma Bargaje Karishma Bargaje and Nikita Khude Nikita Khude},
journal = {International Journal of Creative and Open Research in Engineering and Management},
year = {2026},
doi = {10.55041/ijcope.v2i6.320},
url = {https://doi.org/10.55041/ijcope.v2i6.320}
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