Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
csv
Sub-tasks:
sentiment-classification
Languages:
Kazakh
Size:
100K - 1M
ArXiv:
License:
| 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: | |
| - 100K<n<1M | |
| ## Dataset Description | |
| - **Repository:** https://github.com/IS2AI/KazSAnDRA | |
| - **Paper:** https://arxiv.org/abs/2403.19335 | |
| <h1 align = "center">KazSAnDRA </h1> | |
| <p align = "justify"><b>Kaz</b>akh <b>S</b>entiment <b>An</b>alysis <b>D</b>ataset of <b>R</b>eviews and <b>A</b>ttitudes, or KazSAnDRA, is a <a href = "https://github.com/IS2AI/KazSAnDRA">dataset</a> 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. | |
| </p> | |
| <p> | |
| In the <a href = "https://arxiv.org/abs/2403.19335">original study</a>, KazSAnDRA was utilised for two distinct tasks: | |
| <ol> | |
| <li>polarity classification (PC), involving the prediction of whether a review is positive or negative:</li> | |
| <ul> | |
| <li>reviews with original scores of 1 or 2 were classified as negative and assigned a new score of 0,</li> | |
| <li>reviews with original scores of 4 or 5 were classified as positive and assigned a new score of 1,</li> | |
| <li>reviews with an original score of 3 were categorised as neutral and were excluded from the task.</li> | |
| </ul> | |
| <li>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.</li> | |
| </ol> | |
| </p> | |
| <p align = "justify"> | |
| KazSAnDRA consists of seven CSV files. File <b>full.csv</b> contains all the 180,064 reviews and ratings from 1 to 5. Files <b>train_pc.csv</b>, <b>valid_pc.csv</b>, and | |
| <b>test_pc.csv</b> are the training, validation, and testing sets for the polarity classification task, respectively. Files <b>train_sc.csv</b>, <b>valid_sc.csv</b>, and | |
| <b>test_sc.csv</b> are the training, validation, and testing sets for the score classification task, in turn. | |
| </p> | |
| <p align = "justify"> | |
| All files, except for <b>full.csv</b>, include records containing a custom review identifier (<i>custom_id</i>), the original review text (<i>text</i>), the pre-processed review text (<i>text_cleaned</i>), the corresponding review score (<i>label</i>), and the domain information (<i>domain</i>). | |
| File <b>full.csv</b> includes records containing a custom review identifier (<i>custom_id</i>), the original review text (<i>text</i>), the corresponding review score (<i>label</i>), and the domain information (<i>domain</i>). | |
| </p> | |
| <h2 align = "center">Dataset Statistics</h2> | |
| <p align = "justify">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.</p> | |
| <table align="center"> | |
| <tr align="center"> | |
| <td rowspan="3"><b>Task</b></td> | |
| <td colspan="2"><b>Train</b></td> | |
| <td colspan="2"><b>Valid</b></td> | |
| <td colspan="2"><b>Test</b></td> | |
| <td colspan="2"><b>Total</b></td> | |
| </tr> | |
| <tr></tr> | |
| <tr align="center"> | |
| <td><b>#</b></td> | |
| <td><b>%</b></td> | |
| <td><b>#</b></td> | |
| <td><b>%</b></td> | |
| <td><b>#</b></td> | |
| <td><b>%</b></td> | |
| <td><b>#</b></td> | |
| <td><b>%</b></td> | |
| </tr> | |
| <tr></tr> | |
| <tr align="center"> | |
| <td>PC</td> | |
| <td>134,368</td> | |
| <td>80</td> | |
| <td>16,796</td> | |
| <td>10</td> | |
| <td>16,797</td> | |
| <td>10</td> | |
| <td>167,961</td> | |
| <td>100</td> | |
| </tr> | |
| <tr></tr> | |
| <tr align="center"> | |
| <td>SC</td> | |
| <td>140,126</td> | |
| <td>80</td> | |
| <td>17,516</td> | |
| <td>10</td> | |
| <td>17,516</td> | |
| <td>10</td> | |
| <td>175,158</td> | |
| <td>100</td> | |
| </tr> | |
| </table> | |
| <p align = "justify">The distribution of reviews across the three sets based on their domains and scores for the PC task:</p> | |
| <table align="center"> | |
| <thead> | |
| <tr align="center"> | |
| <th rowspan="3">Domain</th> | |
| <th colspan="2">Train</th> | |
| <th colspan="2">Valid</th> | |
| <th colspan="2">Test</th> | |
| </tr> | |
| <tr></tr> | |
| <tr align="center"> | |
| <th>#</th> | |
| <th>%</th> | |
| <th>#</th> | |
| <th>%</th> | |
| <th>#</th> | |
| <th>%</th> | |
| </tr> | |
| </thead> | |
| <tbody> | |
| <tr align="center"> | |
| <td>Appstore</td> | |
| <td>101,477</td> | |
| <td>75.52</td> | |
| <td>12,685</td> | |
| <td>75.52</td> | |
| <td>12,685</td> | |
| <td>75.52</td> | |
| </tr> | |
| <tr></tr> | |
| <tr align="center"> | |
| <td>Market</td> | |
| <td>22,561</td> | |
| <td>16.79</td> | |
| <td>2,820</td> | |
| <td>16.79</td> | |
| <td>2,820</td> | |
| <td>16.79</td> | |
| </tr> | |
| <tr></tr> | |
| <tr align="center"> | |
| <td>Mapping</td> | |
| <td>6,509</td> | |
| <td>4.84</td> | |
| <td>813</td> | |
| <td>4.84</td> | |
| <td>814</td> | |
| <td>4.85</td> | |
| </tr> | |
| <tr></tr> | |
| <tr align="center"> | |
| <td>Bookstore</td> | |
| <td>3,821</td> | |
| <td>2.84</td> | |
| <td>478</td> | |
| <td>2.85</td> | |
| <td>478</td> | |
| <td>2.85</td> | |
| </tr> | |
| <tr></tr> | |
| <tr align="center"> | |
| <td><b>Total</b></td> | |
| <td><b>134,368</b></td> | |
| <td><b>100</b></td> | |
| <td><b>16,796</b></td> | |
| <td><b>100</b></td> | |
| <td><b>16,797</b></td> | |
| <td><b>100</b></td> | |
| </tr> | |
| </tbody> | |
| </table> | |
| <table align="center"> | |
| <thead> | |
| <tr align="center"> | |
| <th rowspan="3">Score</th> | |
| <th colspan="2">Train</th> | |
| <th colspan="2">Valid</th> | |
| <th colspan="2">Test</th> | |
| </tr> | |
| <tr></tr> | |
| <tr align="center"> | |
| <th>#</th> | |
| <th>%</th> | |
| <th>#</th> | |
| <th>%</th> | |
| <th>#</th> | |
| <th>%</th> | |
| </tr> | |
| </thead> | |
| <tbody> | |
| <tr align="center"> | |
| <td>1</td> | |
| <td>110,417</td> | |
| <td>82.18</td> | |
| <td>13,801</td> | |
| <td>82.17</td> | |
| <td>13,804</td> | |
| <td>82.18</td> | |
| </tr> | |
| <tr></tr> | |
| <tr align="center"> | |
| <td>0</td> | |
| <td>23,951</td> | |
| <td>17.82</td> | |
| <td>2,995</td> | |
| <td>17.83</td> | |
| <td>2,993</td> | |
| <td>17.82</td> | |
| </tr> | |
| <tr></tr> | |
| <tr align="center"> | |
| <td><b>Total</b></td> | |
| <td><b>134,368</b></td> | |
| <td><b>100</b></td> | |
| <td><b>16,796</b></td> | |
| <td><b>100</b></td> | |
| <td><b>16,797</b></td> | |
| <td><b>100</b></td> | |
| </tr> | |
| </tbody> | |
| </table> | |
| <p align = "justify">The distribution of reviews across the three sets based on their domains and scores for the SC task:</p> | |
| <table align="center"> | |
| <thead> | |
| <tr align="center"> | |
| <th rowspan="3">Domain</th> | |
| <th colspan="2">Train</th> | |
| <th colspan="2">Valid</th> | |
| <th colspan="2">Test</th> | |
| </tr> | |
| <tr></tr> | |
| <tr align="center"> | |
| <th>#</th> | |
| <th>%</th> | |
| <th>#</th> | |
| <th>%</th> | |
| <th>#</th> | |
| <th>%</th> | |
| </tr> | |
| </thead> | |
| <tbody> | |
| <tr align="center"> | |
| <td>Appstore</td> | |
| <td>106,058</td> | |
| <td>75.69</td> | |
| <td>13,258</td> | |
| <td>75.69</td> | |
| <td>13,257</td> | |
| <td>75.69</td> | |
| </tr> | |
| <tr></tr> | |
| <tr align="center"> | |
| <td>Market</td> | |
| <td>23,278</td> | |
| <td>16.61</td> | |
| <td>2,909</td> | |
| <td>16.61</td> | |
| <td>2,910</td> | |
| <td>16.61</td> | |
| </tr> | |
| <tr></tr> | |
| <tr align="center"> | |
| <td>Mapping</td> | |
| <td>6,794</td> | |
| <td>4.85</td> | |
| <td>849</td> | |
| <td>4.85</td> | |
| <td>849</td> | |
| <td>4.85</td> | |
| </tr> | |
| <tr></tr> | |
| <tr align="center"> | |
| <td>Bookstore</td> | |
| <td>3,996</td> | |
| <td>2.85</td> | |
| <td>500</td> | |
| <td>2.85</td> | |
| <td>500</td> | |
| <td>2.85</td> | |
| </tr> | |
| <tr></tr> | |
| <tr align="center"> | |
| <td><b>Total</b></td> | |
| <td><b>140,126</b></td> | |
| <td><b>100</b></td> | |
| <td><b>17,516</b></td> | |
| <td><b>100</b></td> | |
| <td><b>17,516</b></td> | |
| <td><b>100</b></td> | |
| </tr> | |
| </tbody> | |
| </table> | |
| <table align="center"> | |
| <thead> | |
| <tr align="center"> | |
| <th rowspan="3">Score</th> | |
| <th colspan="2">Train</th> | |
| <th colspan="2">Valid</th> | |
| <th colspan="2">Test</th> | |
| </tr> | |
| <tr></tr> | |
| <tr align="center"> | |
| <th>#</th> | |
| <th>%</th> | |
| <th>#</th> | |
| <th>%</th> | |
| <th>#</th> | |
| <th>%</th> | |
| </tr> | |
| </thead> | |
| <tbody> | |
| <tr align="center"> | |
| <td>5</td> | |
| <td>101,302</td> | |
| <td>72.29</td> | |
| <td>12,663</td> | |
| <td>72.29</td> | |
| <td>12,663</td> | |
| <td>72.29</td> | |
| </tr> | |
| <tr></tr> | |
| <tr align="center"> | |
| <td>1</td> | |
| <td>20,031</td> | |
| <td>14.29</td> | |
| <td>2,504</td> | |
| <td>14.30</td> | |
| <td>2,504</td> | |
| <td>14.30</td> | |
| </tr> | |
| <tr></tr> | |
| <tr align="center"> | |
| <td>4</td> | |
| <td>9,115</td> | |
| <td>6.50</td> | |
| <td>1,140</td> | |
| <td>6.51</td> | |
| <td>1,139</td> | |
| <td>6.50</td> | |
| </tr> | |
| <tr></tr> | |
| <tr align="center"> | |
| <td>3</td> | |
| <td>5,758</td> | |
| <td>4.11</td> | |
| <td>719</td> | |
| <td>4.10</td> | |
| <td>720</td> | |
| <td>4.11</td> | |
| </tr> | |
| <tr></tr> | |
| <tr align="center"> | |
| <td>2</td> | |
| <td>3,920</td> | |
| <td>2.80</td> | |
| <td>490</td> | |
| <td>2.80</td> | |
| <td>490</td> | |
| <td>2.80</td> | |
| </tr> | |
| <tr></tr> | |
| <tr align="center"> | |
| <td><b>Total</b></td> | |
| <td><b>140,126</b></td> | |
| <td><b>100</b></td> | |
| <td><b>17,516</b></td> | |
| <td><b>100</b></td> | |
| <td><b>17,517</b></td> | |
| <td><b>100</b></td> | |
| </tr> | |
| </tbody> | |
| </table> | |
| <h2 align = "center">How to Use</h2> | |
| <p align = "justify">To load the subsets of KazSAnDRA separately:</p> | |
| ```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") | |
| ``` |