KazSAnDRA

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:
• reviews with original scores of 1 or 2 were classified as negative and assigned a new score of 0,
• reviews with original scores of 4 or 5 were classified as positive and assigned a new score of 1,
• reviews with an original score of 3 were categorised as neutral and were excluded from the task.
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:

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")