Sentiment Analysis
Sentiment Analysis
We build NLP pipelines that transform unstructured text into quantified sentiment signals. Our sentiment analysis solution processes reviews, social media mentions, forum discussions, and support tickets to classify polarity, detect emotion, and extract Named Entities . Every insight is traceable back to source evidence.
Technical Architecture
Our sentiment pipeline combines transformer-based models with lexicon-based fallback systems. We use fine-tuned BERT variants for domain-specific sentiment classification, handling negation, sarcasm, and context-dependent expressions. For high-volume processing, we deploy batch inference with GPU acceleration. Entity recognition extracts brands, products, and people mentioned alongside sentiment signals, enabling granular analysis. All models log prediction confidence for quality filtering.
Data Quality & Validation
Text data is noisy. Our preprocessing pipeline handles emoji interpretation, slang normalization, and language detection. Model outputs get validated against confidence thresholds—low-confidence predictions route to manual review queues. For Data Normalization , we standardize sentiment scores across sources using percentile normalization. Deduplication removes near-duplicate reviews to prevent skewed results from astroturfing or review farms.
Compliance & Ethical Standards
We process only publicly available text data. For any PII detected (emails, phone numbers, personal names), we implement automatic redaction before storage. GDPR and DPDP Act 2023 compliance includes documented retention policies and user data deletion capabilities. We never use personal data beyond the legitimate purpose of sentiment analysis.