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--- |
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license: mit |
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task_categories: |
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- text-classification |
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language: |
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- ru |
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tags: |
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- education |
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pretty_name: UTMN Study Feedbacks ABSA |
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size_categories: |
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- 1K<n<10K |
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--- |
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## Dataset Summary |
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A dataset for training and evaluating models in aspect-based sentiment analysis. It contains student reviews of academic courses written in Russian. |
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## Dataset Structure |
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### Data Fields |
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**Input** |
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* `text`: review text |
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**Output** — sentiment labels for each aspect, the aspect is described in the column name: |
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* `лекции` |
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* `доклады` |
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* `проекты` |
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* `презентации` |
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* `фильмы` |
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* `видео-уроки` |
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* `задания__задачи` |
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* `онлайн-курс` |
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* `баллы__оценки` |
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* `практики__семинары` |
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* `тесты` |
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* `домашняя работа` |
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* `эссе` |
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* `выступления` |
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* `зачет__экзамен` |
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* `материал__информация__темы` |
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* `литература__учебники` |
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* `игры__интерактивность` |
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* `преподаватель` |
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Values in each aspect column are sentiment class labels: |
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* `0`: absent |
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* `1`: neutral |
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* `2`: positive |
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* `3`: negative |
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### Data Splits |
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The dataset is split into three parts: |
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* train |
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* validation |
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* test |
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Random split in proportions: 0.8 / 0.1 / 0.1 |
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| | train | validation | test | |
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| --------------- | ----: | ---------: | ---: | |
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| Input Sentences | 1020 | 127 | 127 | |
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#### Neutral occurrences |
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| Aspect | Train | Validation | Test | |
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| ---------------------------- | ----- | ---------- | ---- | |
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| `баллы__оценки` | 43 | 5 | 7 | |
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| `видео-уроки` | 23 | 4 | 1 | |
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| `выступления` | 27 | 0 | 5 | |
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| `доклады` | 39 | 3 | 6 | |
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| `домашняя работа` | 64 | 11 | 5 | |
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| `задания__задачи` | 91 | 6 | 6 | |
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| `зачет__экзамен` | 33 | 4 | 2 | |
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| `игры__интерактивность` | 19 | 1 | 0 | |
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| `лекции` | 104 | 14 | 10 | |
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| `литература__учебники` | 29 | 1 | 5 | |
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| `материал__информация__темы` | 71 | 8 | 4 | |
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| `онлайн-курс` | 12 | 3 | 1 | |
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| `практики__семинары` | 108 | 10 | 14 | |
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| `презентации` | 87 | 5 | 8 | |
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| `преподаватель` | 85 | 8 | 11 | |
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| `проекты` | 69 | 6 | 7 | |
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| `тесты` | 35 | 6 | 3 | |
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| `фильмы` | 33 | 5 | 3 | |
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| `эссе` | 14 | 1 | 3 | |
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#### Positive occurrences |
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| Aspect | Train | Validation | Test | |
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| ---------------------------- | ----- | ---------- | ---- | |
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| `баллы__оценки` | 188 | 34 | 22 | |
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| `видео-уроки` | 13 | 2 | 2 | |
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| `выступления` | 20 | 0 | 3 | |
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| `доклады` | 6 | 2 | 3 | |
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| `домашняя работа` | 47 | 8 | 4 | |
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| `задания__задачи` | 106 | 10 | 17 | |
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| `зачет__экзамен` | 164 | 29 | 20 | |
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| `игры__интерактивность` | 34 | 2 | 9 | |
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| `лекции` | 79 | 10 | 8 | |
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| `литература__учебники` | 16 | 4 | 2 | |
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| `материал__информация__темы` | 199 | 36 | 33 | |
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| `онлайн-курс` | 16 | 2 | 1 | |
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| `практики__семинары` | 81 | 10 | 8 | |
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| `презентации` | 27 | 5 | 4 | |
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| `преподаватель` | 480 | 69 | 64 | |
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| `проекты` | 20 | 4 | 3 | |
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| `тесты` | 25 | 2 | 4 | |
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| `фильмы` | 11 | 1 | 1 | |
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| `эссе` | 0 | 0 | 1 | |
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#### Negative occurrences |
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| Aspect | Train | Validation | Test | |
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| ---------------------------- | ----- | ---------- | ---- | |
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| `баллы__оценки` | 45 | 6 | 6 | |
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| `видео-уроки` | 5 | 2 | 0 | |
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| `выступления` | 6 | 1 | 2 | |
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| `доклады` | 12 | 1 | 0 | |
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| `домашняя работа` | 16 | 2 | 2 | |
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| `задания__задачи` | 24 | 2 | 2 | |
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| `зачет__экзамен` | 31 | 2 | 4 | |
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| `игры__интерактивность` | 1 | 0 | 0 | |
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| `лекции` | 44 | 2 | 5 | |
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| `литература__учебники` | 12 | 1 | 0 | |
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| `материал__информация__темы` | 50 | 4 | 7 | |
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| `онлайн-курс` | 2 | 2 | 0 | |
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| `практики__семинары` | 13 | 0 | 2 | |
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| `презентации` | 17 | 0 | 1 | |
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| `преподаватель` | 79 | 8 | 11 | |
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| `проекты` | 7 | 2 | 3 | |
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| `тесты` | 15 | 2 | 0 | |
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| `фильмы` | 3 | 0 | 0 | |
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| `эссе` | 4 | 0 | 0 | |
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#### Summary |
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| Split | neutral | positive | negative | |
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| ---------- | ------- | -------- | -------- | |
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| Train | 986 | 1,532 | 386 | |
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| Validation | 101 | 230 | 37 | |
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| Test | 101 | 209 | 45 | |
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## Dataset Creation |
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### Curation Rationale |
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This dataset was created for training aspect-based sentiment analysis models on course review data. Traditional sentiment analysis lacked the granularity needed for educational analytics. |
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### Source Data |
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#### Initial Data Collection and Normalization |
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Reviews were collected from the website: [Otzyvus](https://electives.utmn.ru) (Отзывус) |
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On this platform, students from Tyumen State University leave feedback on elective courses they have taken. |
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All reviews available as of May 6, 2024 were collected. Nonsensical or off-topic reviews were excluded. |
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#### Who are the source language producers? |
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The reviews were written by undergraduate students (typically aged 18–21, though not strictly limited to this range). |
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### Annotations |
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#### Annotation process |
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Aspects were identified using keyphrase extraction across the review corpus. Relevant keywords were grouped into unified aspects. Each aspect was assigned one of the sentiment classes based on the following: |
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* **Positive**: a positive opinion expressed |
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* **Neutral**: the aspect is mentioned, but no clear opinion or a mixed one is expressed |
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* **Negative**: a negative opinion is expressed |
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* **Absent**: the aspect is not mentioned |
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Annotation was done by two annotators, each labeling half of the dataset. There was no cross-annotation, but difficult cases were discussed jointly and resolved with a final verdict. |
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#### Who are the annotators? |
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The annotators were two students from Tyumen State University. |
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### Personal and Sensitive Information |
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No personal information (such as name, faculty, or program) is included in the dataset. |
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## Considerations for Using the Data |
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### Social Impact of Dataset |
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A model trained on this dataset can automatically extract insights into student opinions on the learning process, enabling data-driven decision-making in educational contexts. |
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### Other Known Limitations |
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* The dataset may not be large enough for robust training. |
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* Some aspects are underrepresented. |
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* Additional data or augmentation may be required. |
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* It may be better to use stratified splits instead of random ones. |
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## Additional Information |
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### Dataset Curators |
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* [Albert Fazlyev](https://huggingface.co/bulatovv) |
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* [Danil Krivorogov](https://huggingface.co/danil7) |
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### Licensing Information |
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MIT License |
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### Citation Information |
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``` |
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@misc{fazlyev2024studyfeedbackabsa, |
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author = {Albert Fazlyev and Danil Krivorogov}, |
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title = {A Dataset for Aspect-Based Sentiment Analysis of Russian Student Course Reviews}, |
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year = {2024}, |
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howpublished = {\url{https://huggingface.co/datasets/bulatovv/aspect-sentiment-student-reviews}} |
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} |
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``` |
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### Contributions |
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Thanks to the students who actively left reviews — without you, this dataset would not exist. You are changing the future of education! |
