What are students actually saying about Students' Unions (NSS 2018–2025)?

Students’ comments on Students' Unions are predominantly negative. Around two-thirds of the 2,410 comments carry a negative tone (63.4%), yielding a sentiment index of −19.3. Positives are in the minority (31.9%). Full-time and younger students drive most of the volume, and there are clear differences by sex and subject area.

Scope: UK NSS open‑text comments tagged to Students' Unions across academic years 2018–2025.
Volume: ~2,410 comments; 100% categorised to this topic in this dataset.
Overall mood: 31.9% Positive, 63.4% Negative, 4.7% Neutral (index −19.3; positive:negative ≈ 0.50:1).

What are students saying in this category?

The topic skews negative across the board. Full‑time students (94.6% of comments) are notably downbeat (index −20.3).
Tone differs by sex: males are much more negative (index −30.6; 71.2% negative) than females (−7.0; 54.9% negative).
By ethnicity, White students are more negative (−25.0) than Not UK‑domiciled students (−6.0). Some smaller groups show more balanced tone (Mixed +5.4; Black +4.2), but bases are small.
Subject area variation is substantial. Strongly negative in historical/philosophical/religious studies (−39.4), languages (−38.7), and physical sciences (−37.7). More balanced in psychology (+4.5) and close to neutral in architecture (+1.5), though these are smaller bases.

Trend & benchmarks (segment view)

Demographic snapshot

Segment	Group	n	Pos %	Neg %	Neu %	Sentiment idx
All	—	2410	31.9	63.4	4.7	−19.3
Sex	Male	1275	24.0	71.2	4.8	−30.6
Sex	Female	1085	40.4	54.9	4.7	−7.0
Mode	Full-time	2281	31.3	64.0	4.7	−20.3
Mode	Part-time	84	38.1	57.1	4.8	−8.7
Age	Young	2152	31.6	63.4	4.9	−19.8
Age	Mature	218	29.8	67.4	2.8	−19.9
Disability	Not disabled	1847	31.5	64.0	4.4	−20.0
Disability	Disabled	524	31.5	62.8	5.7	−19.0
Ethnicity	White	1711	28.2	67.3	4.4	−25.0
Ethnicity	Not UK dom.	180	39.4	56.1	4.4	−6.0

Subject‑area variation (CAH1, top by volume)

CAH area (CAH code)	n	Pos %	Neg %	Sentiment idx
Social sciences (CAH15)	290	24.5	68.3	−28.0
Historical, philosophical & religious (CAH20)	187	17.1	76.5	−39.4
Business & management (CAH17)	157	42.7	53.5	−8.3
Computing (CAH11)	112	35.7	58.0	−12.3
Subjects allied to medicine (CAH02)	110	37.3	60.9	−11.3
Engineering & technology (CAH10)	108	28.7	70.4	−25.4
Law (CAH16)	101	39.6	53.5	−13.4
Psychology (CAH04)	94	48.9	48.9	4.5

Note: Small bases can produce volatile indices; interpret rows with low n cautiously.

What this means in practice

Focus on the most negative subject areas

Co‑design a clear engagement plan with schools where tone is most negative: historical/philosophical/religious (−39.4), languages (−38.7), physical sciences (−37.7), social sciences (−28.0), engineering (−25.4).
Actions: targeted course‑level forums, discipline‑specific representation briefings, and a monthly “you said, we did” specific to each school.

Close the gender gap

Males (−30.6; 1,275 comments) are substantially more negative than females (−7.0).
Actions: test alternative messaging and timings, bring representation updates into large compulsory touchpoints, and track uptake/sentiment by cohort.

Learn from relatively balanced pockets

Psychology (+4.5) and areas nearer neutral (e.g., business, allied to medicine, law) offer practices to lift and shift.
Actions: identify the events/services and rep structures that resonate in these groups and replicate in lower‑scoring schools.

Make full‑time engagement easier to see and use

Full‑time students supply 94.6% of comments and remain negative (−20.3).
Actions: publish a simple monthly outcomes digest, set response/service SLAs for common requests, and keep a single, well‑advertised point of contact per school.

How Student Voice Analytics helps you

Track topic tone over time and drill from provider to school/department, campus/site and cohort.
Like‑for‑like comparisons across CAH codes and by demographics (mode, domicile, ethnicity, commuter status).
Rapid, anonymised summaries you can share with SU officers and programme teams.
Export tables and charts for Boards and Student Experience Committees.

FAQs

How is the “sentiment index” calculated?
We score per‑sentence sentiment (−100 to +100), then average within each category/segment.
How are comments assigned to topics?
Each comment is assigned one primary topic (here: Students' Unions). “Share” is that topic’s proportion of all comments; this report focuses only on this topic.

Data at a glance (2018–2025)

Volume: 2,410 comments; 100% with sentiment.
Overall mood: 31.9% Positive, 63.4% Negative, 4.7% Neutral (index −19.3; ≈0.50:1 positive:negative).
Composition highlights: 94.6% full‑time; 89.3% young; 52.9% male, 45.0% female (comment shares).
Strongest negative subject areas: historical/philosophical/religious (−39.4), languages (−38.7), physical sciences (−37.7).
More balanced areas: psychology (+4.5), architecture (+1.5), education (+7.8) — note small bases for some.