Published on: June 2026
ASPECT-AWARE SENTIMENT ANALYSIS OF CODE-MIXED AMAZON INDIAN REVIEWS USING ROBERTA
Nikita Khude Karishma Bargaje Rutuja Kharat Shriraj Ranaware
Prof. D. V. Mehta
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Abstract
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
How to Cite this Paper
Khude, N., Bargaje, K., Kharat, R. & Ranaware, S. (2026). Aspect-Aware Sentiment Analysis of Code-Mixed Amazon Indian Reviews Using Roberta. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(6). https://doi.org/10.55041/ijcope.v2i6.320
Khude, Nikita, et al.. "Aspect-Aware Sentiment Analysis of Code-Mixed Amazon Indian Reviews Using Roberta." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 6, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i6.320.
Khude, Nikita,Karishma Bargaje,Rutuja Kharat, and Shriraj Ranaware. "Aspect-Aware Sentiment Analysis of Code-Mixed Amazon Indian Reviews Using Roberta." International Journal of Creative and Open Research in Engineering and Management 02, no. 6 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i6.320.
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- •Published on: Jun 25 2026
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