Online Review Sentimental Analysis

As the online shopping is growing very fast, business sentiment analysis based on customer reviews is very important for companies. Conventional sentiment analysis tools such as VADER are incapable of picking up high-level language structures, regional idioms, and context-dependent phenomena. This work introduces a state-of-the-art sentiment analysis system using transformer-based architectures such as BERT and RoBERTa, which are fine-tuned over a bespoke e- commerce corpus. The model undertakes Aspect-Based Sentiment Analysis (ABSA) to derive sentiments for particular product features like price, quality, delivery, and customer service. In addition, multilingual support is built using Indic NLP models and Google language detection APIs to support regional languages like Hindi and Tamil. The real-time sentiment stream is built using Apache Kafka, allowing companies to track customer feedback in real-time. Experimental results indicate that the system proposed here is more accurate and relevant than conventional approaches and offers a scalable solution for contemporary e-commerce websites.