Building Trust with Multilingual Sentiment Analysis

Generic sentiment tools often fail in India. Sarcasm, code-mixing, and regional expressions can flip “positive” and “negative” when you only rely on translation.
Awshar AI uses models trained on Indian languages and dialects. That means we capture context: when “bohot hard” means “very difficult” in one context and “very cool” in another, we get it right.
Accurate sentiment isn’t just a nice-to-have-it drives product decisions, campaign tweaks, and crisis response. Multilingual sentiment you can trust starts with data that was built for India.
Read Next
10 Social Listening Metrics Every CMO Should Track
10 social listening metrics every CMO must track, from mention volume to narrative shift. A founder-level breakdown of what the numbers actually mean, why they matter, and how to build a brand intelligence system that anticipates rather than reacts.
ai-conceptsLangChain vs LangGraph: When to Use Which (With Examples)
LangChain vs LangGraph explained simply with real examples. Learn exactly when to use each framework and stop overcomplicating your AI projects.