---
dataset_info:
  features:
  - name: id
    dtype: int64
  - name: text
    dtype: string
  - name: label
    dtype: string
  splits:
  - name: train
    num_bytes: 95263
    num_examples: 696
  - name: validation
    num_bytes: 20394
    num_examples: 149
  - name: test
    num_bytes: 20531
    num_examples: 150
  download_size: 100947
  dataset_size: 136188
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
  - split: test
    path: data/test-*
---

# Dataset Card for BilTweetNews-Sentiment-Analysis

## Table of Contents
- [Dataset Description](#dataset-description)
  - [Dataset Structure](#dataset-structure)
  - [Data Fields](#data-fields)
- [Source Data](#source-data)

## Dataset Description

BilTweetNews sentiment analysis dataset is originally [released](https://huggingface.co/datasets/ctoraman/BilTweetNews-sentiment-analysis).

### Dataset Structure

The original dataset only had `train` split. We applied the following splitting methodology to obtain `validation` and `test` splits:

* If a train-val-test split is available, we use the existing divisions as provided.
* For datasets with a train-test split only, we create a val split from the training set, sized to match the test set, and apply this across all models.
* In cases with a train-val split, we reassign the val set as the test split, then generate a new val split from the training data following the approach above.
* In cases with a val-test split, we split validation into train and vad sets in 80\% and 20\% proportions, respectively.
* When only a single combined split is present, we partition the data into train, val, and test sets in 70\%, 15\%, and 15\% proportions, respectively.

### Data Fields

- **text**(string) : Contains tweets related to Turkish news
- **level**(string) : one of 5 categories: Positive, Negative, Neutral, Sarcastic, Multi

## Source Dataset

[hf.co/datasets/ctoraman/BilTweetNews-sentiment-analysis](https://huggingface.co/datasets/ctoraman/BilTweetNews-sentiment-analysis)