A multi-domain sentiment classification dataset extended from the Business and Reviews datasets supplied by the 2015 Yelp Dataset Challenge.
The parsing functionality requires both datasets to be downloaded and supplied to the parsing script.
The following Python packages must be installed:
- Pandas
- Numpy
- Datasets (HuggingFace)
Run parse.py
with the following, mandatory arguments:
-rf
or--reviews-file
: Filepath to the Yelp Reviews JSON file (str)-bf
or--business-file
: Filepath to the Yelp Business JSON file (str)-out
or--output-dir
: Filepath to the directory where the datasets will be saved.
Domain | Positive-class Samples | Negative-class Samples | Total Samples |
---|---|---|---|
Restaurants | 1,673,359 | 971,671 | 2,645,030 |
Food | 698,971 | 420,512 | 1,119,483 |
Nightlife | 579,360 | 306,257 | 885,617 |
Event Planning & Services | 199,582 | 146,431 | 346,013 |
Shopping | 120,920 | 142,777 | 263,697 |
Hotels & Travel | 98,421 | 108,977 | 207,398 |
Arts & Entertainment | 120,967 | 66,981 | 187,948 |
Beauty & Spas | 56,199 | 92,444 | 148,643 |
Automotive | 28,589 | 91,244 | 119,833 |
Home Services | 22,972 | 91,698 | 114,670 |
Active Life | 55,747 | 39,946 | 95,693 |
Local Services | 26,406 | 67,872 | 94,278 |
Health & Medical | 22,861 | 63,712 | 86,573 |
Local Flavor | 50,576 | 19,919 | 70,495 |
Real Estate | 7,612 | 27,346 | 34,958 |
Pets | 11,188 | 21,926 | 33,114 |
Professional Services | 6,374 | 20,134 | 26,508 |
Public Services & Government | 10,922 | 7,469 | 18,391 |
Education | 7,398 | 6,445 | 13,843 |
Financial Services | 2,280 | 11,276 | 13,556 |
Religious Organizations | 1,625 | 657 | 2,282 |
Mass Media | 926 | 1,320 | 2,246 |