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💬 '''Sentiment analysis''' is a [[Definition:Natural language processing (NLP) | natural language processing]] technique that insurance organizations use to extract and classify opinions, emotions, and attitudes from unstructured text — including [[Definition:Claims | claims]] notes, customer reviews, call-center transcripts, social media posts, and survey responses. Within the insurance sector, the technology helps carriers and [[Definition:Insurtech | insurtech]] firms convert vast volumes of qualitative data into actionable signals about policyholder satisfaction, [[Definition:Fraud detection | fraud]] indicators, and market perception.

⚙️ Algorithms trained on labeled datasets assign polarity scores (positive, negative, neutral) and, in more advanced implementations, detect specific emotions such as frustration, urgency, or confusion. An insurer's [[Definition:Claims management | claims]] operation might deploy sentiment analysis on adjuster notes and claimant communications to flag files where dissatisfaction is escalating — allowing supervisors to intervene before a complaint becomes litigation. [[Definition:Underwriting | Underwriting]] teams can analyze broker feedback at scale to identify pain points in submission workflows, while marketing departments monitor social channels to gauge reaction to product launches or [[Definition:Rate filing | rate changes]]. The models often integrate with broader [[Definition:Artificial intelligence (AI) | AI]] pipelines, feeding sentiment scores into [[Definition:Predictive analytics | predictive models]] that forecast [[Definition:Policyholder retention | retention]], [[Definition:Claims severity | severity]], or [[Definition:Net Promoter Score (NPS) | NPS]] outcomes.

💡 As customer experience becomes a sharper competitive differentiator, the ability to systematically measure sentiment gives insurers an edge that gut instinct alone cannot provide. Regulatory scrutiny around [[Definition:Treating customers fairly (TCF) | fair treatment of customers]] also makes it prudent to have objective, auditable measures of how policyholders perceive their interactions with a carrier. Early adopters in the [[Definition:Insurtech | insurtech]] space have shown that integrating sentiment signals into [[Definition:Claims triage | claims triage]] or [[Definition:Customer relationship management (CRM) | CRM]] platforms can reduce complaint ratios and improve [[Definition:Customer lifetime value (CLV) | customer lifetime value]], making the technique relevant well beyond its origins in consumer marketing.

'''Related concepts:'''
{{Div col|colwidth=20em}}
* [[Definition:Natural language processing (NLP)]]
* [[Definition:Artificial intelligence (AI)]]
* [[Definition:Predictive analytics]]
* [[Definition:Fraud detection]]
* [[Definition:Customer experience (CX)]]
* [[Definition:Claims management]]
{{Div col end}}

Definition:Sentiment analysis - Revision history

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