## Dataset Summary
A dataset for training and evaluating models in aspect-based sentiment analysis. It contains student reviews of academic courses written in Russian.
## Dataset Structure
### Data Fields
**Input**
* `text`: review text
**Output** — sentiment labels for each aspect, the aspect is described in the column name:
* `лекции`
* `доклады`
* `проекты`
* `презентации`
* `фильмы`
* `видео-уроки`
* `задания__задачи`
* `онлайн-курс`
* `баллы__оценки`
* `практики__семинары`
* `тесты`
* `домашняя работа`
* `эссе`
* `выступления`
* `зачет__экзамен`
* `материал__информация__темы`
* `литература__учебники`
* `игры__интерактивность`
* `преподаватель`
Values in each aspect column are sentiment class labels:
* `0`: absent
* `1`: neutral
* `2`: positive
* `3`: negative
### Data Splits
The dataset is split into three parts:
* train
* validation
* test
Random split in proportions: 0.8 / 0.1 / 0.1
| | train | validation | test |
| --------------- | ----: | ------…
