Sentiment score

Sentiment analysis scores the emotional tone of a conversation to flag risk and measure experience without waiting for a survey response. Applied across every contact rather than a sampled few, it covers the large majority of customers who never complete a satisfaction survey at all.

Brand sentiment is the classified emotional valence of brand mentions across reviews, social posts, and support conversations. Mentions are typically sorted as positive, negative, neutral, or mixed. Net sentiment is calculated as the share of positive mentions minus the share of negative mentions, a structure similar to Net Promoter Score. Brand sentiment analysis relies on machine learning or language models to classify text at scale. Documented limitations remain: sarcasm, mixed statements within a single review, and category-specific slang can all lower classification accuracy. A single sentiment score is directional, not representative of the full customer base. It should be read alongside mention volume and topic breakdown.

CSAT, or customer satisfaction score, is a post-contact rating averaged across responses and usually collected with a single question immediately after an interaction closes. Because only a small fraction of customers respond, CSAT reflects the experience of people motivated to answer rather than the whole contact population.

Text analytics automates the process of extracting structure and meaning from unstructured written feedback: support tickets, chat transcripts, survey verbatims, reviews, and social posts. Core techniques include classifying text into predefined topic taxonomies, extracting named entities such as products, features, and locations, modeling topics to surface unlabeled themes, and tagging a sentiment score to gauge tone. The practical output is a count of how often each issue appears and how that frequency trends over time. Results are only as useful as the underlying taxonomy: a vague or inconsistent category structure produces noisy, hard-to-action data. OnClarity’s Voice of Customer applies these techniques across feedback aggregated from 100+ sources.