What is Text Analytics?
Text analytics is the set of techniques that turns open‑text answers into measurable insights — e.g., themes, sentiment and keywords — so teams can understand what people actually say at scale. It combines language processing, pattern detection and simple statistics to summarize free‑text responses.
Text analytics (also called text mining or natural language processing for feedback) analyzes written responses — comments, suggestions, complaints and free‑text survey answers — to extract useful information. Common steps include cleaning text, detecting language, translating when needed, identifying keywords and topics, grouping similar answers, and scoring sentiment. For non‑technical users this means thousands of open‑ended replies can be reduced to a handful of themes, example quotes and basic metrics that make qualitative data easier to act on.
Usage example
A council runs a multilingual consultation and receives 2,000 open comments across ten languages. Hearo translates replies into the admin's language, runs text analytics to surface top concerns (housing, safety, parking), shows representative quotes for each theme and highlights areas with most negative sentiment — so the team can prioritise follow‑up without reading every response.
Practical application
Text analytics matters because it makes free‑text responses actionable. Instead of manually reading every comment, organisations can: find recurring problems, measure public sentiment over time, compare themes across demographic groups or languages, prioritise issues that need human review, and produce concise reports with example quotes. In Hearo's multilingual context, combined translation and analytics lets teams hear voices they otherwise couldn't process and improve translation quality where wording affects meaning.
FAQ
How accurate is text analytics across multiple languages?
Accuracy depends on the language, the quality of translation and the complexity of responses. Short, clear comments are easier to categorise than slang or coded language. Hearo reduces language barriers by translating responses into the admin language before analysis, and improves results over time through human review and correction of translations. For critical decisions, combine automated summaries with spot checks by native speakers.
Do I need to label responses or train models first?
No — basic text analytics uses unsupervised methods (keyword extraction, topic clustering, sentiment scoring) that work without labelled training data. If you want higher precision, you can create custom dictionaries, map synonyms, or provide a small labelled sample to train a customised classifier.
Can text analytics identify sensitive or personal information?
Yes — tools can flag likely personal identifiers (names, addresses, contact details) using entity recognition, but automated detection isn't perfect. Hearo recommends configuring PII filters, obtaining clear consent, and reviewing flagged items manually before sharing or publishing results.
Will text analytics replace reading individual responses?
No. Text analytics is a triage and summarisation tool: it surfaces themes, trends and representative quotes so you can focus human attention where it matters. For nuance, context or complex cases, reading the original response and involving native speakers is still important.