arafat 

SentimentAnalyzer is an on-device (offline) open-source library for sentiment analysis using ML.NET. For a lightweight alternative using VADER (rule-based), see SentimentAnalyzer.Core. For high-accuracy... More information

Lightweight transformer-based sentiment analysis using TinyBERT (~18MB). 94.2% accuracy on Amazon reviews. Fully offline with embedded model. English-only, binary classification (positive/negative). For... More information

SentimentAnalyzer.Core is a lightweight, on-device (offline) sentiment analysis library using VADER (rule-based). Perfect for mobile, IoT, and edge scenarios. Add SentimentAnalyzer.Onnx for higher accuracy with... More information

High-accuracy multilingual sentiment analysis using DistilBERT transformer. Supports 104 languages with 3-class classification (positive/neutral/negative). NOTE: The ~270MB model is downloaded on first use and... More information