Student Comment Analysis
The Student Comment Analysis page brings together the open-ended responses students submitted in the survey, alongside AI-generated sentiment scoring and thematic tagging. It is designed for wellbeing leads and coordinators who want to move beyond scores and understand what students are actually saying — in their own words — and what those comments might signal about their experience at school.
The sentiment analysis on this page was generated using artificial intelligence. While it provides a useful starting point, AI analysis may occasionally contain inaccuracies or misinterpretations. Always read the comment itself and apply your own professional judgement before drawing conclusions or taking action.
Page Layout
The page is divided into three areas.
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The left panel contains a scrollable student list showing each student's name alongside their sentiment classification, intensity score, and whether they have been flagged as needing attention. Three filter dropdowns above the list let you narrow the list by priority, sentiment, or theme.
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The centre panel is the comment detail view. Selecting a student from the list loads their individual record here, showing their written comment, the AI-assigned theme and wellbeing scores, and a breakdown of their peer relationships from the survey.
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The right panel contains two summary widgets that apply across the whole cohort: a Sentiment Distribution pie chart and a Keywords Highlight word cloud.
Filters
Three filters sit above the student list. You can use them individually or in combination to focus on specific groups.
Filter by Priority
Narrows the list to students flagged at a particular priority level.
| Option | What it shows |
|---|---|
| High | Students whose comment or data signals an elevated concern requiring closer attention. |
| Normal | Students whose comment does not raise an immediate concern. |
Filter by Sentiment
Narrows the list by the AI-assigned overall sentiment of the student's comment.
| Option | What it shows |
|---|---|
| Negative | Students whose comment was classified as expressing a negative sentiment. |
| Positive | Students whose comment was classified as expressing a positive sentiment. |
Filter by Theme
Narrows the list to students whose comment was tagged under a specific theme. Available themes are:
| Theme | What it covers |
|---|---|
| Academic Wellbeing | Comments relating to learning, workload, school performance, or academic experience. |
| Extracurricular Activities | Comments relating to sports, clubs, or activities outside the classroom. |
| School Rules/Policies | Comments relating to school rules, fairness, or how policies are applied. |
| School Staff | Comments relating to teachers, support staff, or school leadership. |
| Social Wellbeing | Comments relating to friendships, belonging, exclusion, or peer relationships. |
| Survey | Comments relating to the survey itself rather than school experience. |
| No Comment / no comment | Students who did not submit a written comment. |
To clear any active filter, click the × on the filter chip. Filters stack — applying both Negative sentiment and High priority shows only students who match both conditions.
Student List
The student list displays every student who submitted a survey response. Each row shows four columns.
- Name — The student's full name. Click any row to load that student's comment detail in the centre panel.
- Sentiment — The AI-assigned sentiment for the student's comment: Positive or Negative.
- Inten... (Intensity) — A numeric score indicating how strongly the sentiment is expressed, on a scale from 1 to 10. A higher score means a more strongly worded comment.
- Needs At... (Needs Attention) — Indicates whether the student has been flagged as requiring follow-up. Displays Yes or No.
Students flagged as Needs Attention: Yes typically appear at the top of the list by default, giving you a fast view of the most pressing cases.
Comment Detail Panel
Selecting a student from the list opens their full record in the centre panel. The panel header shows the student's name, with ← and → navigation arrows to move to the previous or next student in the current filtered list without returning to the list each time.
Searching for a Student
Click the search icon (🔍) in the panel header to open a searchable dropdown. Type a name or scroll to find and jump directly to any student in the current filtered list.
Comment Details
Each student record shows the following fields.
Your Comments displays the student's verbatim written response from the survey. This is the most important field on the page — read it in full before acting on any of the scores below it.
Theme shows the topic category the AI assigned to the comment, drawn from the same set of themes available in the Filter by Theme dropdown.
The three wellbeing scores below the comment reflect the student's survey responses across three dimensions and are colour-coded to indicate their level:
- Mental Wellbeing — The student's self-reported mental health score. Displayed in green when High, in standard text when Average or Low.
- Social Wellbeing — The student's self-reported social connectedness score. Displayed in green when High.
- Academic Wellbeing — The student's self-reported academic experience score. Displayed in green when High.
These scores are drawn from the survey data and are separate from the AI sentiment analysis. They give you context for interpreting the comment — a student who writes positively but scores Low on Mental Wellbeing warrants closer attention than the sentiment alone would suggest.
Peer Relationship Badges
Below the wellbeing scores, a scrollable section shows the student's peer relationships from the survey, grouped by relationship type. Each name appears as a badge with a directional arrow indicating the nature of the nomination:
- ↓ arrow — This student nominated that peer (outgoing nomination).
- ↑ arrow — That peer nominated this student (incoming nomination).
Relationship groups you may see include:
| Group | What it means |
|---|---|
| Friends | Students this student identified as friends, or who identified this student as a friend. |
| Disrespect | Peers involved in disrespectful interactions — either directed at this student or by this student toward others. |
| Influential | Peers this student sees as influential, or who see this student as influential. |
| Feedback | Peers this student would go to for feedback, or who nominated this student for that role. |
The arrow direction matters when reviewing concerns. An incoming disrespect nomination (↑) means another student named this student as someone who disrespected them. An outgoing disrespect nomination (↓) means this student named someone else.
Cross-referencing a student's comment with their peer relationships often reveals patterns that are not visible in either piece of data alone. A student who writes about feeling unwelcome and has no incoming friend nominations may benefit from a social connection intervention.
Sentiment Distribution
The Sentiment Distribution pie chart in the right panel shows the proportion of positive versus negative comments across the entire cohort currently shown in the student list. It updates as you apply filters, so filtering to a specific year level or theme will adjust the chart to reflect only those students.
The chart gives you a fast read on the overall tone of the cohort. A cohort with a large negative segment warrants a closer look at what themes are driving that sentiment.
Keywords Highlight
The Keywords Highlight word cloud aggregates the most frequently used words from all student comments in the current filtered view. Words that appear more often are displayed larger. The colour of each word corresponds to its sentiment — words associated with positive expressions appear in one colour, and words associated with negative expressions appear in another.
Use the word cloud to identify recurring topics that may not be captured in individual themes. If words like "lonely", "excluded", or "stress" appear prominently, they point to areas worth exploring further in the individual comments.
How to Use This Page
The Student Comment Analysis page is most useful when you treat the AI classifications as a starting point rather than a conclusion. The scores and flags direct your attention — the comment itself and your knowledge of the student should drive any decision.
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Start with the Needs Attention list. Apply the High priority filter and work through each flagged student in order. Open their record, read the comment in full, and look at the wellbeing scores and peer relationship badges together before drawing any conclusions.
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Read the comment before acting on the scores. A Negative sentiment with a high intensity score is significant — but the written comment tells you what the concern is and gives you the context that scores alone cannot. Never action a student based on sentiment classification alone.
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Use Filter by Theme to spot shared concerns. If you want to know whether an issue is isolated to one student or shared across the cohort, filter by a theme — for example, School Staff or Social Wellbeing — to see all students who raised comments in that area. A cluster of similar concerns often points to a systemic issue worth addressing at the year-level rather than student by student.
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Cross-reference the peer relationship badges with the comment. A student who writes about feeling excluded but has no incoming friend nominations is telling you the same story from two different angles. Use both pieces of information together when deciding whether and how to follow up.
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Review the Keywords Highlight word cloud for patterns the filters miss. Students often describe the same underlying concern in different ways. If words like lonely, excluded, stressed, or unfair appear prominently across the cohort, they point to themes worth exploring further in individual records — even if those students are not flagged as High priority.
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Check the Sentiment Distribution to understand the overall picture. Before reviewing individual comments, the pie chart gives you a quick read on the cohort's overall tone. A large negative segment warrants a more systematic review; a predominantly positive result still warrants checking the flagged students.
Comments and sentiment data on this page relate to student wellbeing. Follow your school's student welfare and privacy policies when recording, sharing, or acting on this information.
Bring the Student Comment Analysis into your wellbeing team meetings alongside the Student Dashboard and Year Level Dashboard. Individual comments gain additional meaning when you can cross-reference them with a student's network position and wellbeing scores from the wider report.