
The dataset is relevant to Ukrainian reviews in three different domains:
1) Hotels.
2) Reustarants.
3) Products.

The dataset is comrpised of several .csv files, which one can found useful:
1) processed_data.csv - the processed dataset itself.
2) train_val_test_indices.csv - csv file with train/val/test indices. The split was stratified w.r.t dataset name (hotels, reustarants, products) and rating.
3) bad_ids.csv - csv file with ids of bad samples marked using model filtering approach, only ids of those samples for which difference between actual and predicted rating is bigger than 2 points are maintained in this file.


The data is scrapped from Tripadvisor (https://www.tripadvisor.com/) and Rozetka (https://rozetka.com.ua/).

The dataset was initially used for extraction of key-phrases relevant to one of rating categories, based on trained machine learning model (future article link will be here).

Dataset is processed to include two additional columns: one with lemmatized tokens and another one with POS tags. Both lemmatization and POS tagging are done using pymorphy2 (https://pymorphy2.readthedocs.io/en/stable/) library.

The words are tokenized using a specific regex tokenizer to account for usage of apostroph.

Those reviews which weren't in Ukrainian were translated to it using Microsoft translator and re-checked manually afterwards.


task_categories:text-classification size_categories:100K<n<1M language:uk license:cc region:us
{"language": ["uk"], "license": "cc", "size_categories": ["100K<n<1M"], "task_categories": ["text-classification"]}
TAGS
#task_categories-text-classification #size_categories-100K<n<1M #language-Ukrainian #license-cc #region-us 


The dataset is relevant to Ukrainian reviews in three different domains:
1) Hotels.
2) Reustarants.
3) Products.

The dataset is comrpised of several .csv files, which one can found useful:
1) processed_data.csv - the processed dataset itself.
2) train_val_test_indices.csv - csv file with train/val/test indices. The split was stratified w.r.t dataset name (hotels, reustarants, products) and rating.
3) bad_ids.csv - csv file with ids of bad samples marked using model filtering approach, only ids of those samples for which difference between actual and predicted rating is bigger than 2 points are maintained in this file.


The data is scrapped from Tripadvisor (URL and Rozetka (URL

The dataset was initially used for extraction of key-phrases relevant to one of rating categories, based on trained machine learning model (future article link will be here).

Dataset is processed to include two additional columns: one with lemmatized tokens and another one with POS tags. Both lemmatization and POS tagging are done using pymorphy2 (URL library.

The words are tokenized using a specific regex tokenizer to account for usage of apostroph.

Those reviews which weren't in Ukrainian were translated to it using Microsoft translator and re-checked manually afterwards.


TAGS
#task_categories-text-classification #size_categories-100K<n<1M #language-Ukrainian #license-cc #region-us 

passage: TAGS
#task_categories-text-classification #size_categories-100K<n<1M #language-Ukrainian #license-cc #region-us 
