UzMedSentiment

Dataset Summary

UzMedSentiment is an Uzbek medical-domain dataset for sentiment classification, aspect-based sentiment analysis, and auxiliary cue detection. The current release contains 4,791 annotated rows in a single TSV file.

Each row includes:

• source metadata
• a medical-domain text snippet
• an aspect label
• a 3-way sentiment label
• a 5-point polarity score
• adverse drug reaction (ADR) and severity annotations
• negation, speculation, sarcasm, and cue-span annotations

Supported Tasks

• sentiment classification
• aspect-based sentiment analysis (ABSA)
• ADR signal detection
• negation detection
• speculation detection
• sarcasm detection

Files

• UzMedSentiment.tsv: main release file in tab-separated format

Dataset Structure

Columns

Column	Type	Description
id	integer	Unique identifier
source	categorical	Data source such as telegram, instagram, forum, web
lang	categorical	Language/script tag in the released file
text	string	De-identified patient comment or medical-domain text
aspect	categorical	Aspect label
sentiment	categorical	POS, NEG, or NEU
polarity_score	integer	Polarity intensity in the range -2 to +2
adr_flag	binary	0 or 1, whether an ADR is present
severity	categorical	ADR severity
negation	binary	0 or 1
speculation	binary	0 or 1
sarcasm	binary	0 or 1
cue_span	string	Cue phrase such as a negation or speculation trigger

Tagsets

Sentiment Labels

Label	Meaning	Description	Example
POS	Positive	Helpful, convenient, or beneficial outcome	Dori yordam berdi.
NEG	Negative	Complaint, harm, problem, or adverse outcome	Navbat juda uzun.
NEU	Neutral	Question, factual statement, or neutral information	Bu dori xavfsizmi?

Polarity Intensity

Score	Meaning	Description	Example
2	Strong positive	Very good result or clear benefit	Ajoyib natija!
1	Positive	Good or satisfactory outcome	Og‘riq kamaydi.
0	Neutral	Neutral statement or question	Dorini ichdim.
-1	Negative	Negative experience or mild complaint	Yon ta’sir paydo bo‘ldi.
-2	Strong negative	Severe complaint or very bad experience	Bu dori juda yomon ta’sir qildi.

Aspect Labels

Aspect	Description	Example context
dori	Drug, medicine, or pharmacotherapy	tabletka, sirop, kapsula, ukol
simptom	Clinical symptom reported by the patient	og‘riq, isitma, yo‘tal, toshma
muolaja	Treatment process or intervention	fizioterapiya, in’eksiya, davolanish
diagnostika	Test, screening, or diagnostic process	analiz, test, rentgen, UTT
shifokor-munosabati	Doctor or staff behavior and communication	shifokor, hamshira, muomala
xizmat	Administrative or service interaction	registratura, yozilish, operator
narx	Financial aspect	narx, to‘lov, arzon/qimmat
kutish-vaqti	Waiting time or delay	navbat, kechikish, tezkorlik
infratuzilma	Facility or physical conditions	palata, joylashuv, tozalik, sovuq
parhez	Diet or regimen	ovqat, rejim, parhez

Clinical Risk and Linguistic Cues

Field	Values	Meaning
adr_flag	0, 1	Whether an adverse drug reaction is present
severity	engil, o‘rta, og‘ir, null	Severity of the adverse event
negation	0, 1	Negation is present
speculation	0, 1	Speculation or uncertainty is present
sarcasm	0, 1	Sarcasm or irony is present
cue_span	free text	Trigger phrase such as ehtimol, hech qanday, yo‘q

Statistics

Overview

• Rows: 4,791
• Columns: 13
• Split: train only
• Average text length: 132.12 characters
• Median text length: 96 characters
• Maximum text length: 1,549 characters
• Non-empty cue_span: 3,663

Sentiment Distribution

Label	Count
NEG	1,970
NEU	1,783
POS	1,038

Aspect Distribution

Aspect	Count
simptom	1,319
muolaja	978
diagnostika	626
dori	541
xizmat	469
shifokor-munosabati	424
infratuzilma	197
narx	104
kutish-vaqti	77
parhez	53

Source Distribution

Source	Count
forum	3,688
telegram	927
instagram	89
web	74
web-komment	13

Additional Annotation Counts

Field	Positive / non-default value	Count
adr_flag	1	158
negation	1	816
speculation	1	1,321
sarcasm	1	48

Notes on the Current Release

• No official train, validation, or test split is provided.
• The id column is normalized to sequential unique values from 1 to 4,791.
• Text content was whitespace-normalized in the current release: tab, carriage return, and newline characters were replaced with spaces, and repeated whitespace was collapsed.
• The lang column is heterogeneous in the released TSV and contains both uz and uz-latin.
• The official aspect tagset contains 10 labels, but the released TSV also includes three outlier aspect values: Simptom, ijtimoiy, and adr_flag.
• The official severity tagset is engil, o‘rta, og‘ir, and null, but a small number of rows contain jiddiy, yuqori, 0, and o'rta.
• One row still uses a Unicode minus sign in polarity_score (−1) instead of ASCII -1.

These inconsistencies are minor in count, but users may want to normalize labels before training or evaluation.

Usage

from datasets import load_dataset

dataset = load_dataset(
    "csv",
    data_files={"train": "UzMedSentiment.tsv"},
    delimiter="\t",
    encoding="utf-8-sig",
)

print(dataset["train"][0])

License

This dataset is released under the CC-BY-4.0 license.