Understand What Your Customers Really Feel

We can analyze data from social media (Twitter, Facebook, Instagram, LinkedIn), review sites (Google, Trustpilot, product-specific), customer support tickets, chat transcripts, emails, survey responses, news articles, and any text data you provide via API or bulk upload.

Our models achieve 94%+ accuracy on standard sentiment classification (positive/negative/neutral). For domain-specific applications with custom training, we typically see 90%+ accuracy. We provide confidence scores for each classification so you can handle edge cases appropriately.

Yes, we train domain-specific models for each client. Healthcare, finance, technical products — each has its own vocabulary. We start with pre-trained models and fine-tune on your specific data and terminology for maximum accuracy.

Sentiment analysis focuses on the emotional tone — positive, negative, neutral, or specific emotions. Text analytics is broader and includes topic extraction, entity recognition, keyword analysis, and summarization. We typically combine both for comprehensive insight extraction.

We have specialized models for code-mixed content (English-Hindi, English-Spanish, etc.) and offer multilingual support across 50+ languages. For mixed content, we use language detection first, then apply the appropriate model, and can even analyze sentiment for each language component separately.

Sarcasm detection requires deep contextual understanding. We use transformer-based models trained on millions of examples of sarcastic content, combined with punctuation, capitalization, and emoji signals. Our models flag potentially sarcastic content with confidence scores so you can review edge cases.