AI Sentiment Analysis

Overview#

A customer rating of three stars tells you almost nothing. The text that accompanies it tells you everything: frustration with a specific feature, gratitude for a resolution, confusion about a process, or eroding trust in a brand. The AI Sentiment Analysis platform extracts that nuance systematically, analysing text at document, paragraph, sentence, and aspect levels to reveal not just whether sentiment is positive or negative but which specific topics drive it and with what emotional intensity.

Purpose-built for customer experience teams, brand monitoring operations, and market research functions, this system turns unstructured feedback into structured, actionable intelligence.

Key Features#

Multi-Level Sentiment Classification: Analyses sentiment at document, paragraph, sentence, and aspect levels on a five-point scale with confidence scores, providing understanding from macro trends down to specific pain points.
Granular Emotion Detection: Identifies 27 distinct emotions beyond simple positive/negative, including joy, frustration, trust, confusion, gratitude, and anticipation, with intensity scoring for each.
Aspect-Based Sentiment Analysis: Extracts sentiment toward specific product features, attributes, and aspects mentioned in text, pinpointing what customers like or dislike about individual components rather than treating feedback as a single undifferentiated signal.
Contextual Understanding: Handles sarcasm, negation, intensity modifiers, and comparative sentiment with nuanced interpretation rather than surface-level keyword matching.
Multi-Language Support: Consistent analysis across dozens of languages with cross-lingual transfer learning for global operations.
Real-Time Monitoring: Streaming analysis pipeline identifies negative sentiment spikes and emerging issues as they occur, enabling rapid response before problems compound.
Temporal Sentiment Tracking: Monitors sentiment evolution over time within conversations, documents, and across broader trend periods.
Emotion-Based Prioritisation: Routes customer interactions based on detected emotional urgency, ensuring distressed or frustrated customers receive faster attention.
Aspect Hierarchies: Organises sentiment into structured categories and subcategories for systematic product and service analysis.

Use Cases#

Customer Experience Management: Monitors sentiment across reviews, support tickets, and social media to detect emerging issues in real time and prioritise response based on emotional urgency. Contact centres use emotion-based routing to connect high-distress callers with experienced agents.
Brand Reputation Monitoring: Tracks emotional associations with your brand across channels and languages, identifying shifts in perception before they become entrenched trends that require expensive correction.
Product Development Feedback: Uses aspect-based analysis to understand exactly which features drive satisfaction or frustration, informing roadmap prioritisation with data rather than assumptions about what users want.
Employee Sentiment Analysis: Analyses survey responses and internal feedback with granular emotion detection to reveal actionable workforce insights beyond simple satisfaction scores. Useful for organisations managing large distributed workforces, including public safety agencies monitoring dispatcher and responder welfare.

Integration#

The platform integrates with CRM systems, social media monitoring platforms, survey tools, review platforms, and contact centre solutions. Pre-built connectors enable rapid deployment across existing feedback collection channels.

Open Standards#

GraphQL (June 2018 specification): All sentiment analysis queries and mutations are exposed through a GraphQL API, enabling typed, schema-driven access to results, statistics, and real-time analysis requests.
JSON Web Token (RFC 7519) / RS256 (RFC 7518): API access is authorised via JWTs verified with RS256 asymmetric signing, enforcing tenant-scoped permission checks on every sentiment query and mutation.
ISO 8601 / RFC 3339: All analysis timestamps (the analyzed_at field and related temporal trend data) are stored and exchanged in ISO 8601 UTC format to ensure unambiguous cross-system interoperability.
Unicode / UTF-8 (ISO/IEC 10646): Input text processing and multi-language analysis operates on Unicode-encoded content, enabling consistent handling of dozens of languages and non-Latin scripts across global deployments.
ISO 639 language codes / BCP 47 (RFC 5646): Multi-language support relies on IETF language tags for identifying and routing text to the appropriate cross-lingual analysis pipeline.
OpenAPI 3.x (OAS3): The REST complement to the GraphQL interface conforms to the OpenAPI specification, providing machine-readable API contracts for CRM, survey tool, and contact centre integrations.
OAuth 2.0 (RFC 6749): Integration with third-party platforms (CRM systems, social media monitoring, review platforms) uses OAuth 2.0 authorisation flows to obtain delegated access tokens without exposing user credentials.

Last Reviewed: 2026-02-23 Last Updated: 2026-04-14