task_categories:text-classification task_ids:sentiment-classification multilinguality:monolingual size_categories:100K<n<1M language:tr license:cc-by-sa-4.0 region:us
Customer reviews dataset for Turkish. Includes reviews for vitamins and supplement products,crawled from e-commerce website Vitaminler.com. All reviews are in Turkish.[Vitamins and Supplements Customer Reviews Dataset](https://github.com/turkish-nlp-suite/Vitamins-Supplements-Reviews)
@inproceedings{altinok-2023-diverse,
    title = "A Diverse Set of Freely Available Linguistic Resources for {T}urkish",
    author = "Altinok, Duygu",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-long.768",
    pages = "13739--13750",
    abstract = "This study presents a diverse set of freely available linguistic resources for Turkish natural language processing, including corpora, pretrained models and education material. Although Turkish is spoken by a sizeable population of over 80 million people, Turkish linguistic resources for natural language processing remain scarce. In this study, we provide corpora to allow practitioners to build their own applications and pretrained models that would assist industry researchers in creating quick prototypes. The provided corpora include named entity recognition datasets of diverse genres, including Wikipedia articles and supplement products customer reviews. In addition, crawling e-commerce and movie reviews websites, we compiled several sentiment analysis datasets of different genres. Our linguistic resources for Turkish also include pretrained spaCy language models. To the best of our knowledge, our models are the first spaCy models trained for the Turkish language. Finally, we provide various types of education material, such as video tutorials and code examples, that can support the interested audience on practicing Turkish NLP. The advantages of our linguistic resources are three-fold: they are freely available, they are first of their kind, and they are easy to use in a broad range of implementations. Along with a thorough description of the resource creation process, we also explain the position of our resources in the Turkish NLP world.",
}
---
language:
- tr
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
task_categories:
- text-classification
task_ids:
  - sentiment-classification
pretty_name: Vitamins and Supplements Customer Reviews Dataset
---

# Dataset Card for turkish-nlp-suite/vitamins-supplements-reviews

<img src="https://raw.githubusercontent.com/turkish-nlp-suite/.github/main/profile/supplements.png"  width="20%" height="20%"> 

### Dataset Description
- **Repository:** [Vitamins and Supplements Reviews Dataset](https://github.com/turkish-nlp-suite/Vitamins-Supplements-Reviews)
- **Paper:** [ACL link](https://aclanthology.org/2023.acl-long.768/)
- **Dataset:** Vitamins and Supplements Reviews Dataset
- **Domain:** E-commerce, customer reviews


### Dataset Summary
Turkish sentiment analysis dataset from customer reviews about supplement and vitamin products. The dataset is scraped from Vitaminler.com and contains
customer reviews and star rating about vitamin and supplement products.

Each customer review in the Vitamins and Supplements Reviews Dataset describes a customer’s experience with a supplement product in terms of the product’s effectiveness,
side effects, taste and smell, as well as comments on supplement usage frequency and dosage, active ingredients, brand, and similar products by other brands. The reviews
also include pointers to customers’ health history and indications how the supplements helped in resolving customers’ health problems.

Considering the characteristics of the data, our Vitamins and Supplements Reviews Dataset lies at the intersection of customer review data and healthcare NLP data. We hope
to offer a finely compiled medical NLP dataset for Turkish NLU.


### Dataset Instances
The dataset includes 1,052 products of 262 distinct brands with 244K customer reviews. During the compilation, we eliminated reviews containing person names such as customer's name and influencer names
Each dataset instance contains 

- product name
- brand name
- customer review text
- star rating

Here's an example  for you:

```
{
  "product_name": "Microfer Şurup 250 ml",
  "brand": "Ocean",
  "review": "Bittikçe alıyorum harika bişey kızım tadını da seviyo",
  "star": 5
}
```

If you're rather interested in JSON format where reviews are accumulated by product name, you can find the dataset as a single JSON in dataset's [Github repo](https://github.com/turkish-nlp-suite/Vitamins-Supplements-Reviews).


### Data Split

|  name   |train|validation|test|
|---------|----:|---:|---:|
|Vitamins and Supplements Reviews|200866|20000|20000|



### Citation
This work is supported by Google Developer Experts Program. Part of Duygu 2022 Fall-Winter collection, "Turkish NLP with Duygu"/ "Duygu'yla Türkçe NLP". All rights reserved. If you'd like to use this dataset in your own work, please kindly cite [A Diverse Set of Freely Available Linguistic Resources for Turkish](https://aclanthology.org/2023.acl-long.768/) :

```
@inproceedings{altinok-2023-diverse,
    title = "A Diverse Set of Freely Available Linguistic Resources for {T}urkish",
    author = "Altinok, Duygu",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-long.768",
    pages = "13739--13750",
    abstract = "This study presents a diverse set of freely available linguistic resources for Turkish natural language processing, including corpora, pretrained models and education material. Although Turkish is spoken by a sizeable population of over 80 million people, Turkish linguistic resources for natural language processing remain scarce. In this study, we provide corpora to allow practitioners to build their own applications and pretrained models that would assist industry researchers in creating quick prototypes. The provided corpora include named entity recognition datasets of diverse genres, including Wikipedia articles and supplement products customer reviews. In addition, crawling e-commerce and movie reviews websites, we compiled several sentiment analysis datasets of different genres. Our linguistic resources for Turkish also include pretrained spaCy language models. To the best of our knowledge, our models are the first spaCy models trained for the Turkish language. Finally, we provide various types of education material, such as video tutorials and code examples, that can support the interested audience on practicing Turkish NLP. The advantages of our linguistic resources are three-fold: they are freely available, they are first of their kind, and they are easy to use in a broad range of implementations. Along with a thorough description of the resource creation process, we also explain the position of our resources in the Turkish NLP world.",
}
```
