UzSentiNER v1: A 30,000-Sentence Uzbek Corpus for Joint Sentiment Analysis and NER (6 Entity Types) with Emoji Signals

Description

UzSentiNER v1 is a 30,000-sentence Uzbek corpus designed for joint sentiment analysis (Positive/Negative/Neutral) and named entity recognition with six entity types: PERSON, POSITION, ORGANIZATION, LOCATION, DATE, PRODUCT. The dataset combines 24,000 synthetic sentences (generated and calibrated with two rounds of manual quality control) and 6,000 linguist-curated sentences, merged into a single unified schema. Emoji usage is included to simulate real user writing patterns; emojis are placed naturally within sentences (not appended at the end). The package provides multiple formats for reproducible research: XLSX, CSV, JSONL, plus token-level BIO tags and CoNLL (train/val/test) exports for NER training. Character-level entity spans are also included. A manual audit on a random subset of 1,000 sentences reports 95.1% acceptable samples; remaining issues relate only to sentiment calibration, with 0% NER-type errors observed. This dataset is intended for benchmarking and developing Uzbek NLP models for sentiment classification, NER, and multi-task learning. See README and documentation files for schema, annotation guidelines, validation report, and reproducibility instructions.

Steps to reproduce

Download and unzip UzSentiNER_v1_30K_FULL.zip. Open the dataset files in data/ (available as .xlsx, .csv, .jsonl). For NER training, use the CoNLL exports in conll/: uzsentiNER_v1_train.conll, uzsentiNER_v1_val.conll, uzsentiNER_v1_test.conll. For span-based NER, use entity_spans (character offsets) in the tabular/JSONL files. To verify integrity and recompute statistics, use: stats/stats_summary_v1.json (reference statistics), stats/sha256_checksums_v1.csv (file hashes), optional scripts in scripts/ (see docs/REPRODUCE.md). Manual QC evidence is provided in qc/ (random 1,000-sentence audit).

Institutions

• Urgench State University

  Xorazm Region, Urgench
• Novosibirsk State University

  Novosibirsk Oblast, Novosibirsk

Categories

Licence