Datasetpublic

avramandrei/RoIt-XMASA

RoIt-XMASA Romanian–Italian Cross-domain Multi-domain Sentiment Analysis RoIt-XMASA is a multilingual, cross-domain sentiment analysis dataset containing user reviews in Romanian (RO) and Italian (IT) across three domains: Books, Movies, and Music. Reviews are annotated with 1–5 star ratings (excluding 3). Dataset Summary Split Rows Labeled train 12,000 yes validation 12,000 yes test 12,000 yes unlabeled 202,141 no Splits… See the full description on the dataset page: https://huggingface.co/datasets/avramandrei/RoIt-XMASA.

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Dataset Card

RoIt-XMASA

Romanian–Italian Cross-domain Multi-domain Sentiment Analysis

RoIt-XMASA is a multilingual, cross-domain sentiment analysis dataset containing user reviews in Romanian (RO) and Italian (IT) across three domains: Books, Movies, and Music. Reviews are annotated with 1–5 star ratings (excluding 3).

Dataset Summary

Split	Rows	Labeled
train	12,000	yes
validation	12,000	yes
test	12,000	yes
unlabeled	202,141	no

Splits breakdown (labeled)

Each labeled split is perfectly balanced:

—Domains: 4,000 reviews per domain (Books / Movies / Music)
—Languages: 6,000 reviews per language (RO / IT)

Data Fields

Field	Type	Description
`id`	int64	Unique review identifier
`title`	string	Review title (may be empty)
`text`	string	Review body
`domain`	string	Domain: `Books`, `Movies`, or `Music`
`language`	string	Language code: `RO` (Romanian) or `IT` (Italian)
`rating`	int64	Star rating: 1, 2, 4, or 5 (null for unlabeled)

Usage

python

from datasets import load_dataset

ds = load_dataset("avramandrei/RoIt-XMASA")

# Labeled splits
train = ds["train"]
val   = ds["validation"]
test  = ds["test"]

# Filter by language or domain
ro_books = train.filter(lambda x: x["language"] == "RO" and x["domain"] == "Books")