New Emotion Dataset Captures How Ghanaian Students Really Feel About University Life

Every semester, university students around the world ride an emotional rollercoaster that universities themselves rarely see in real time. Stress before exams, elation after results, frustration with administration, pride in achievements — these feelings shape academic performance, mental health, and whether students thrive or drop out. Now, a team of researchers in Ghana has built a tool designed to make those emotions visible to machines: a curated dataset called EduAffect, published in the journal Discover Education, that captures the emotional texture of student life at one of West Africa’s largest universities, complete with the code-switched English and Twi that students actually speak online.

The dataset, assembled by Francisca Adoma Acheampong and colleagues at the Kwame Nkrumah University of Science and Technology (KNUST) in Kumasi, contains 2,423 entries, each tagged with one of 28 fine-grained emotion labels — 27 distinct emotions plus a neutral class — drawn from the widely used GoEmotions framework. Every entry also maps onto a broader sentiment category of positive, negative, or neutral, allowing researchers to work at two levels of granularity. What makes EduAffect unusual is not just its labels but its provenance: rather than recycling generic internet text, the team harvested authentic student discourse from social media platforms and paired it with structured survey responses from students themselves.

The social media component came from X (formerly Twitter), Facebook, and TikTok, collected between August 2023 and February 2024 — a window deliberately chosen to capture the emotional peaks of the academic calendar. The collection period spanned mid-semester and end-of-semester examinations, Student Representative Council elections, social gatherings, innovation showcases, and even unfortunate campus incidents. On X, the researchers tracked university-related hashtags such as #KNUST and #KNUSTChronicles and monitored established student-facing accounts. Facebook contributed public student groups and official university pages, while TikTok supplied student-led discussions about academic pressure and campus events. The result is a corpus anchored to the real rhythms of university life rather than an arbitrary snapshot of online chatter.

Ensuring that the posts genuinely came from students, rather than staff, advertisers, or the general public, required a multi-stage validation process. The team first filtered accounts to prioritise those that predominantly shared university-related content or were active members of verified public student forums. They then aligned collection with major campus milestones — examination periods, registration windows, results release dates — to anchor the discourse in the institutional student experience. Finally, every entry was manually reviewed to strip out commercial advertisements, external news reports, promotional material, and posts from non-student entities such as university administration pages. A second data stream, collected through anonymous Google Forms surveys, invited students to reflect in their own words on academic pressures, campus life, and coping strategies, complementing the spontaneous tone of social media with more considered reflections.

The preprocessing pipeline reveals just how much technical care goes into building a trustworthy emotion dataset. All text was lowercased and cleaned of URLs, user mentions, and non-informative symbols; repetitive punctuation was collapsed, so that “KNUST!!!” became “knust!”. Entries shorter than three or longer than fifty tokens were filtered out, a range chosen because most entries naturally fall between 10 and 40 tokens and prior research has found that 99 percent of geotagged social media posts contain fewer than 52 words. Duplicate and near-duplicate entries — texts sharing identical meaning despite minor punctuation differences — were removed to prevent skewed frequency bias. Privacy protections were built in throughout: no names, student ID numbers, hall locations, or direct usernames were retained, and only publicly visible posts were ever collected, under ethical approval from KNUST’s Humanities and Social Sciences Research Ethics Committee.

The most linguistically interesting challenge was the frequent code-switching between English and Twi, a widely spoken Ghanaian language that remains low-resource in natural language processing. Generic models trained on Reddit-style data routinely fail to interpret the affective weight of local interjections and pragmatic markers such as “y3 y3 dom” or “aba”. To handle this, the team adopted a rule-based, expert-driven method: a panel of three native Twi linguists reviewed every code-switched utterance, preserving high-impact terms that carry strong emotional or cultural meaning and lack direct English equivalents, while translating the rest. Translations were then verified through back-translation checks, in which an independent speaker converted the English back into Twi to confirm that the original emotional and semantic weight survived. For example, the expression “what dem go do the school bea make them do aba” was rendered as “whatever they feel like doing to the school, let them do”.

Even profanity was handled with a two-tier system designed to preserve emotional signal without tolerating harm. Mild profanity, often a marker of genuine frustration — as in “this exam is f*ing hard” — was retained because it serves as a high-signal indicator of a student’s affective state. Extreme profanity, defined as language targeting protected characteristics such as race, religion, gender, or sexual orientation, or maliciously threatening specific individuals, was removed. This distinction reflects a growing consensus in affective computing that sanitising text too aggressively can strip out exactly the emotional intensity researchers are trying to measure.

Annotation itself was a two-stage affair blending lived experience with clinical expertise. Seven students purposively selected from the KNUST body were trained on formal guidelines defining all 28 emotion categories and instructed to assign the single most dominant emotion to each entry — a practice known as hard labelling, which produces an unambiguous ground truth for supervised learning. Inter-annotator agreement, measured with Fleiss’ Kappa across all 2,423 instances, came to 0.5735, which falls within the “moderate agreement” band on the widely cited Landis and Koch scale — a robust result, the authors argue, for a taxonomy as fine-grained as 28 categories. A clinical psychologist then reviewed every student-labelled entry, revising 17 percent of the initial labels, with systematic disagreements concentrated in semantically overlapping pairs such as “annoyance” versus “disapproval” and “optimism” versus “approval”.

The resulting label distribution offers a statistical portrait of student emotion in public discourse. Approval dominates with 524 entries, followed by neutral (346), optimism (266), disapproval (214), and annoyance (154) — a concentration reflecting how student talk revolves around institutional judgments, future expectations, and interpersonal tensions. Disappointment (132) and joy (104) also feature prominently, capturing the twin currents of frustration and aspiration. At the other extreme, love (2), remorse (1), and disgust (5) are vanishingly rare, likely because public platforms discourage expressions of intimate or vulnerable states. The authors are candid about the implications: they recommend that practical applications consolidate the 28 labels into 5 to 7 broader “core pedagogical affect” categories, reserving the full schema for specialised studies using hierarchical modelling or few-shot learning to cope with extreme class imbalance.

The team positions EduAffect as a corrective to a persistent blind spot in emotion research. Landmark resources such as GoEmotions, with over 58,000 Reddit comments, and EmoBank, with its valence-arousance-dominance ratings, are built on general internet discourse with pronounced Western cultural biases and little connection to academic settings. EduAffect, released publicly on Kaggle under a CC BY 4.0 license, is the first curated resource to centre the West African student experience, explicitly accounting for English-Twi code-switching. The authors acknowledge limitations — the single dominant label per entry can flatten co-occurring emotions, the data come from one institution, and temporal or event-based analysis is not yet possible without event metadata — and they outline a forward agenda that includes cross-cultural validation, longitudinal tracking of student emotions, fine-tuning transformer models such as BERT and RoBERTa on the corpus, and eventually building real-time counselling assistants, emotion dashboards, and early-warning systems. If those tools materialise, the candid, code-switched voices of Ghanaian students could help machines finally understand what university life feels like from the inside.

Subject of Research: A curated emotion-labelled dataset of Ghanaian university students' experiences for affective computing research

Article Title: A curated dataset for emotion-based analysis of students’ experiences in higher education

Article References: Acheampong, F. A., Nunoo-Mensah, H., Dei-Asamoa, R., Nurideen, F. I., & Kakah, E. S. (2026). A curated dataset for emotion-based analysis of students’ experiences in higher education. Discover Education, 5(1), Article 816. https://doi.org/10.1007/s44217-026-02098-1

Image Credits: AI Generated

DOI: 10.1007/s44217-026-02098-1

Keywords: EduAffect dataset, affective computing, emotion recognition, natural language processing, sentiment analysis, higher education, student mental health, Ghana, Twi language, code-switching, GoEmotions, KNUST