The Citation Sentiment Analysis System goes beyond citation counts by analyzing whether references are positive, negative, or neutral. Using NLP and interactive visualizations, it helps researchers and institutions gain deeper insights into scholarly impact. Users can upload PDFs, extract citations, analyze sentiment, and visualize trends with real-time updates and customizable options.
PDF Upload & Text Extraction
Citation Sentiment Analysis
Real-Time Processing
Interactive Visualizations
Error Handling & File Support
Export & Sharing Options
Provides AI-driven insights about citation trends, helping researchers identify impactful works and research gaps.
Uses Natural Language Processing (NLP) and Machine Learning (ML) to classify citations as positive, negative, or neutral based on contextual meaning.
AI-powered Optical Character Recognition (OCR) extracts text from scanned PDFs, ensuring that even complex academic documents are processed efficiently.
The system continuously learns from new data and user feedback, refining its AI model to enhance accuracy, detect nuanced sentiments, and improve citation sentiment classification over time.
CiteSense - Citation Sentiment Analysis