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:
- Hotels.
- Reustarants.
- Products.
The dataset is comrpised of several .csv files, which one can found useful:
- processed_data.csv - the processed dataset itself.
- 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.
- 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
- 56