alaminxpro/university-students-complaints

University Students Complaints Dataset

A labeled dataset of 332 real-world complaints collected from university students across multiple departments. Each complaint is annotated with category, severity, responsible departments, and fine-grained aspect labels — making it suitable for multi-label text classification, severity prediction, and department routing tasks.

Dataset Summary

University students regularly raise concerns about infrastructure, academics, technical facilities, administration, and finances. This dataset captures those complaints in English and provides structured labels that can be used to train models for automated complaint triage, severity assessment, and routing to the correct department.

Key characteristics:

• Collected from students across 6 departments (CSE, EEE, IPE, BBA, LAW, ENG)
• 5 complaint categories: Infrastructure, Technical, Academic, Administrative, Finance
• 4 severity levels: Low, Medium, High, Urgent
• Multi-label annotations for responsible departments and complaint aspects
• Paraphrase grouping via Complaint_Group_ID — multiple phrased versions of the same complaint share a group ID, useful for paraphrase detection and data augmentation studies

Supported Tasks

Task	Description
Complaint Category Classification	Classify complaints into one of 5 categories (Infrastructure, Technical, Academic, Administrative, Finance)
Severity Prediction	Predict the urgency level (Low, Medium, High, Urgent)
Department Routing	Predict which department(s) should handle the complaint
Multi-label Aspect Classification	Identify which aspects a complaint covers (WiFi, Washroom, Classroom, etc.)
Paraphrase Detection	Leverage Complaint_Group_ID to study paraphrase relationships

Data Fields

Field	Type	Description
ID	int	Unique complaint identifier
Timestamp	string	When the complaint was submitted
Gender	string	Gender of the student (Male / Female)
Semester	float	Academic semester of the student (e.g., 1.2, 4.1)
Student_Dept	string	Department the student belongs to (CSE, EEE, IPE, BBA, LAW, ENG)
Complaint_Description	string	Free-text complaint written by the student
Category	string	Complaint category: Infrastructure, Technical, Academic, Administrative, Finance
Responsible_Departments	string	Pipe-separated list of departments responsible (e.g., IT | Maintenance)
Primary_Department	string	The single most relevant department
Aspects	string	Pipe-separated aspect labels (e.g., WiFi | Classroom)
Severity	string	Severity level: Low, Medium, High, Urgent
Complaint_Group_ID	int	Groups paraphrased versions of the same complaint

Dataset Statistics

Splits

Split	Samples
Train	273
Test	59
Total	332

Category Distribution

Category	Count
Infrastructure	143
Technical	65
Academic	64
Finance	33
Administrative	27

Severity Distribution

Severity	Count
Urgent	108
Medium	99
High	98
Low	27

Student Department Distribution

Department	Count
CSE	245
EEE	29
BBA	15
ENG	15
IPE	14
LAW	14

Top Complaint Aspects

Aspect	Count
Electrical/AC	163
Classroom	97
Faculty	37
Washroom	37
Examination	35
WiFi	32
Fee/Payment	32
Lab Equipment	28
Projector	22
Parking	22

Unique Complaint Groups

There are 186 unique complaint groups — many original complaints have paraphrased variants, providing a natural resource for studying textual similarity and paraphrase generation.

Usage

from datasets import load_dataset

dataset = load_dataset("alaminxpro/university-students-complaints")

# Access splits
train = dataset["train"]
test = dataset["test"]

# Example: view first complaint
print(train[0]["Complaint_Description"])
print(train[0]["Category"], train[0]["Severity"])

Working with Multi-label Fields

The Responsible_Departments and Aspects fields use " | " (pipe with spaces) as a separator:

aspects = train[0]["Aspects"].split(" | ")
departments = train[0]["Responsible_Departments"].split(" | ")

Additional Files

• university_students_complaints.xlsx — The original dataset in Excel format

Author

Created by Md. Al Amin — LinkedIn

Citation

If you use this dataset in your research or projects, please cite it as:

@dataset{alamin2026university,
  title={University Students Complaints Dataset},
  author={Md. Al Amin},
  year={2026},
  url={https://huggingface.co/datasets/alaminxpro/university-students-complaints},
  note={ORCID: 0009-0005-5859-3093},
  license={CC-BY-4.0}
}

License

This dataset is released under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.