--- pretty_name: Kazakh Sentiment Analysis Dataset of Reviews and Attitudes dataset_info: - config_name: full features: - name: custom_id dtype: string - name: text dtype: string - name: label dtype: class_label: names: 0: "0 stars" 1: "1 star" 2: "2 stars" 3: "3 stars" 4: "4 stars" 5: "5 stars" - name: domain dtype: string splits: - name: train num_bytes: 24381051 num_examples: 180064 - config_name: polarity_classification features: - name: custom_id dtype: string - name: text dtype: string - name: text_cleaned dtype: string - name: label dtype: class_label: names: 0: "negative" 1: "positive" - name: domain dtype: string splits: - name: train num_bytes: 32618403 num_examples: 134368 - name: validation num_bytes: 4085072 num_examples: 16796 - name: test num_bytes: 4285278 num_examples: 16797 - config_name: score_classification features: - name: custom_id dtype: string - name: text dtype: string - name: text_cleaned dtype: string - name: label dtype: class_label: names: 0: "1 star" 1: "2 stars" 2: "3 stars" 3: "4 stars" 4: "5 stars" - name: domain dtype: string splits: - name: train num_bytes: 34107559 num_examples: 140126 - name: validation num_bytes: 4318229 num_examples: 17516 - name: test num_bytes: 4235569 num_examples: 17516 configs: - config_name: full data_files: - split: train path: "full/full.csv" default: true - config_name: polarity_classification data_files: - split: train path: "polarity_classification/train_pc.csv" - split: validation path: "polarity_classification/valid_pc.csv" - split: test path: "polarity_classification/test_pc.csv" - config_name: score_classification data_files: - split: train path: "score_classification/train_sc.csv" - split: validation path: "score_classification/valid_sc.csv" - split: test path: "score_classification/test_sc.csv" license: cc-by-4.0 task_categories: - text-classification task_ids: - sentiment-classification language: - kk size_categories: - 100KKazSAnDRA

Kazakh Sentiment Analysis Dataset of Reviews and Attitudes, or KazSAnDRA, is a dataset developed for Kazakh sentiment analysis. KazSAnDRA comprises a collection of 180,064 reviews obtained from various sources and includes numerical ratings ranging from 1 to 5, providing a quantitative representation of customer attitudes.

In the original study, KazSAnDRA was utilised for two distinct tasks:

  1. polarity classification (PC), involving the prediction of whether a review is positive or negative:
  2. score classification (SC), where the objective was to predict the score of a review on a scale ranging from 1 to 5. To align with the enumeration used for labelling in the classifier, which starts from 0 rather than 1, labels 1–5 were transformed into 0–4.

KazSAnDRA consists of seven CSV files. File full.csv contains all the 180,064 reviews and ratings from 1 to 5. Files train_pc.csv, valid_pc.csv, and test_pc.csv are the training, validation, and testing sets for the polarity classification task, respectively. Files train_sc.csv, valid_sc.csv, and test_sc.csv are the training, validation, and testing sets for the score classification task, in turn.

All files, except for full.csv, include records containing a custom review identifier (custom_id), the original review text (text), the pre-processed review text (text_cleaned), the corresponding review score (label), and the domain information (domain). File full.csv includes records containing a custom review identifier (custom_id), the original review text (text), the corresponding review score (label), and the domain information (domain).

Dataset Statistics

For the sake of maintaining consistency and facilitating reproducibility of our experimental outcomes among different research groups, we partitioned KaZSAnDRA into three distinct sets: training (train), validation (valid), and testing (test) sets, following an 80/10/10 ratio.

Task Train Valid Test Total
# % # % # % # %
PC 134,368 80 16,796 10 16,797 10 167,961 100
SC 140,126 80 17,516 10 17,516 10 175,158 100

The distribution of reviews across the three sets based on their domains and scores for the PC task:

Domain Train Valid Test
# % # % # %
Appstore 101,477 75.52 12,685 75.52 12,685 75.52
Market 22,561 16.79 2,820 16.79 2,820 16.79
Mapping 6,509 4.84 813 4.84 814 4.85
Bookstore 3,821 2.84 478 2.85 478 2.85
Total 134,368 100 16,796 100 16,797 100
Score Train Valid Test
# % # % # %
1 110,417 82.18 13,801 82.17 13,804 82.18
0 23,951 17.82 2,995 17.83 2,993 17.82
Total 134,368 100 16,796 100 16,797 100

The distribution of reviews across the three sets based on their domains and scores for the SC task:

Domain Train Valid Test
# % # % # %
Appstore 106,058 75.69 13,258 75.69 13,257 75.69
Market 23,278 16.61 2,909 16.61 2,910 16.61
Mapping 6,794 4.85 849 4.85 849 4.85
Bookstore 3,996 2.85 500 2.85 500 2.85
Total 140,126 100 17,516 100 17,516 100
Score Train Valid Test
# % # % # %
5 101,302 72.29 12,663 72.29 12,663 72.29
1 20,031 14.29 2,504 14.30 2,504 14.30
4 9,115 6.50 1,140 6.51 1,139 6.50
3 5,758 4.11 719 4.10 720 4.11
2 3,920 2.80 490 2.80 490 2.80
Total 140,126 100 17,516 100 17,517 100

How to Use

To load the subsets of KazSAnDRA separately:

```python from datasets import load_dataset full = load_dataset("issai/kazsandra", "full") pc = load_dataset("issai/kazsandra", "polarity_classification") sc = load_dataset("issai/kazsandra", "score_classification") ```