uznlp-uz/absa-edu

Uzbek Education Aspect-Based Sentiment Analysis

Uzbek Education Aspect-Based Sentiment Analysis (absa-edu) is an aspect-level sentiment dataset containing Uzbek-language opinions about education. Each row provides a text sample, an aspect term and category, an opinion expression, sentiment and polarity labels, intensity, context, negation, irony, and script information.

Dataset Summary

• —Dataset ID: uznlp-uz/absa-edu
• —Language: Uzbek (uz)
• —Domain: Education
• —Rows: 7,499
• —Columns: 14
• —Aspect categories: 30
• —Sentiment classes: 4 (NEG, POS, MIX, NEU)
• —Splits: train, validation, test
• —Format: UTF-8 TSV
• —License: CC BY 4.0

Data Files

The three TSV splits have no overlapping IDs. Their union is identical to the set of IDs in full.tsv.

Data Fields

Label Values

Sentiment

Polarity Score

Statistics

Overview

Sentiment Distribution

Aspect Category Distribution

Polarity Score Distribution

Intensity Distribution

Negation and Irony

Script Distribution

Loading

Load the predefined splits from the Hugging Face Hub:

from datasets import load_dataset

dataset = load_dataset("uznlp-uz/absa-edu")
print(dataset)
print(dataset["train"][0])

Load an individual split:

from datasets import load_dataset

train = load_dataset("uznlp-uz/absa-edu", split="train")
validation = load_dataset("uznlp-uz/absa-edu", split="validation")
test = load_dataset("uznlp-uz/absa-edu", split="test")

Load the full TSV file directly:

from datasets import load_dataset

full_dataset = load_dataset(
    "csv",
    data_files="full.tsv",
    delimiter="\t",
    split="train",
)

With pandas:

import pandas as pd

train = pd.read_csv("train.tsv", sep="\t")
validation = pd.read_csv("val.tsv", sep="\t")
test = pd.read_csv("test.tsv", sep="\t")
full_dataset = pd.read_csv("full.tsv", sep="\t")

Intended Use

The dataset can be used for Uzbek aspect-based sentiment analysis research and development, including:

• —sentiment classification;
• —aspect category classification;
• —aspect term and opinion expression analysis;
• —polarity and intensity prediction;
• —negation and irony analysis;
• —multilingual and multi-script NLP experiments;
• —education-domain NLP benchmarking.

Notes and Limitations

• —The dataset is specific to the education domain; results may not generalize to other domains.
• —A text may occur more than once when it is associated with different aspect-level annotations.
• —The Polarity_score value 0 is used by both neutral and mixed examples; use Sentiment when these classes must be distinguished.
• —Script labels are normalized to Latin, Cyrillic, and Mixed.
• —Users should independently evaluate model behavior for dialectal, orthographic, and code-mixed Uzbek text before deployment.

License

This dataset is released under the Creative Commons Attribution 4.0 International license.

Citation

If you use this dataset, cite the dataset repository:

@misc{uzbek_absa_edu_2026,
  title        = {Uzbek Education Aspect-Based Sentiment Analysis},
  author       = {{UzNLP}},
  year         = {2026},
  howpublished = {\url{https://huggingface.co/datasets/uznlp-uz/absa-edu}},
  license      = {CC BY 4.0}
}