Classify comments in sector language
HE-specific categories make the outputs easier for academic and professional teams to interpret.
Feature
Classify student comments with a taxonomy designed for UK higher education rather than generic customer experience text.
Student Voice Analytics uses an HE-specific taxonomy for student comments, covering themes such as assessment, teaching, support, resources, organisation, student voice, community, and wider experience.
Discuss the source data, reporting question, outputs and delivery window.
Survey teams, quality teams, insight analysts, and governance stakeholders.
Generic text analytics tools often use categories built for consumer, employee, or general sentiment work. Student comments need a taxonomy that understands HE language, survey context, and institutional reporting needs.
HE-specific categories make the outputs easier for academic and professional teams to interpret.
A stable taxonomy supports comparison across schools, years, surveys, and sector benchmarks.
Sentence-level classification helps one comment contribute to several relevant categories when students mention multiple issues.
Student comments use HE-specific language and relate to survey, quality, and regulatory contexts that generic taxonomies can miss.
Yes. Category outputs can be reviewed and corrected where local context suggests a better interpretation.
No. A comment can contain several sentences and themes, so sentence-level classification can capture more than one issue.
Discuss the source data, reporting question and outputs your team needs. Explore the worked example before agreeing the scope and delivery window.
Discuss your survey