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@article{200951,
        author = {Mrs. B. Lakshmidevi and Gopiga S and Monica M and Lavanya S},
        title = {Context-Aware Sentiment Intelligence and Personalized Product Suggestions Using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {no},
        pages = {131-141},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=200951},
        abstract = {This project presents a context-aware sentiment analysis and brand popularity monitoring system for digital commerce platforms. The system collects customer reviews and social media opinions from platforms like Amazon, Flipkart, Twitter, and Instagram. Using Natural Language Processing (NLP) techniques and a fine-tuned BERT model, it classifies sentiments accurately as positive, negative, or neutral.The system performs aspect-based sentiment analysis to identify product-level strengths and weaknesses. It extracts meaningful insights from large volumes of unstructured text data. These insights help businesses understand customer expectations and market trends.An interactive dashboard built using Flask and MySQL visualizes brand popularity scores, sentiment trends, and engagement metrics in real time. The system supports manufacturers, retailers, and customers with intelligent product recommendations.Overall, the project provides an automated, scalable, and intelligent decision-support solution for e-commerce analytics.},
        keywords = {Sentiment Analysis, BERT, NLP, Brand Popularity Analytics, Aspect-Based Analysis, Digital Commerce, Machine Learning, Recommendation System, Data Mining, Dashboard Analytics.},
        month = {May},
        }