index
int64
0
663k
split
stringclasses
3 values
0
train
1
test
2
train
3
train
4
train
5
test
6
train
7
train
8
train
9
train
10
test
11
test
12
train
13
train
14
train
15
train
16
val
17
train
18
train
19
train
20
train
21
train
22
val
23
val
24
train
25
train
26
test
27
train
28
train
29
train
30
train
31
test
32
train
33
train
34
train
35
val
36
train
37
train
38
train
39
test
40
train
41
train
42
train
43
train
44
train
45
train
46
train
47
test
48
val
49
train
50
train
51
train
52
train
53
train
54
train
55
test
56
train
57
test
58
train
59
train
60
train
61
train
62
train
63
train
64
train
65
val
66
val
67
val
68
train
69
train
70
val
71
train
72
train
73
train
74
train
75
test
76
train
77
train
78
train
79
train
80
train
81
train
82
train
83
test
84
train
85
train
86
train
87
train
88
val
89
train
90
train
91
train
92
train
93
test
94
train
95
val
96
train
97
train
98
train
99
test

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.

Downloads last month
47
Edit dataset card

Models trained or fine-tuned on vkovenko/cross_domain_uk_reviews