---
language:
- ro
- it
license: cc-by-4.0
task_categories:
- text-classification
task_ids:
- sentiment-classification
pretty_name: RoIt-XMASA
size_categories:
- 100K<n<1M
tags:
- sentiment-analysis
- cross-lingual
- cross-domain
- romanian
- italian
- reviews
---

# 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")
```