README.md

---
annotations_creators:
- Duygu Altinok
language:
- tr
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
pretty_name: MusteriYorumlari
tags:
- sentiment
dataset_info:
  features:
  - name: text
    dtype: string
  - name: label
    dtype: 
      class_label:
        names:
          0: 1_star
          1: 2_star
          2: 3_star
          3: 4_star
          4: 5_star
  splits:
  - name: train
    num_bytes: 46979645
    num_examples: 73920
  - name: validation
    num_bytes: 733500
    num_examples: 15000
  - name: test
    num_bytes: 742661
    num_examples: 15000
  download_size: 58918801
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/valid-*
  - split: test
    path: data/test-*
---


# MüşteriYorumlari - A Large Scale Customer Sentiment Analysis Dataset for Turkish 


<img src="https://raw.githubusercontent.com/turkish-nlp-suite/.github/main/profile/musteriyorumlarilogo.png"  width="30%" height="30%">


## Dataset Summary

MüşteriYorumları is a Turkish e-commerce customer reviews dataset of size 103K, scraped from Hepsiburada.com and Trendyol.com. These reviews encompass a wide
array of product categories, including apparel, food items, baby products, and books. Review stars are in range of 1-5 stars.

The star distribution is as follows:


| star rating | count |
|---|---|
| 1  | 12,873 |
| 2  | 11,472 |
| 3  | 18,054 |
| 4  | 31,207 |
| 5  | 30,314 |
| total | 103,920 |

The star distribution is quite skewed towards 4+ reviews. For more information about dataset statistics, please refer to the [research paper]().

## Dataset Instances

An instance looks like:

```
{
"text": "SÜPEEEER KALİTE",
"label": 4   #5 stars
}
```

## Data Split

|  name   |train|validation|test|
|---------|----:|---:|---:|
|MüşteriYorumları Customer Reviews|73920|15000|15000|


## Benchmarking
This dataset is a part of [SentiTurca](https://huggingface.co/datasets/turkish-nlp-suite/SentiTurca) benchmark, in the benchmark the subset name is **e-commerce**, named according to the GLUE tasks.
Model benchmarking information can be found under SentiTurca HF repo and benchmarking scripts can be found under [SentiTurca Github repo](https://github.com/turkish-nlp-suite/SentiTurca).

For this dataset we benchmarked a transformer based model BERTurk and a handful of LLMs. Success of each model is follows:

| Model | acc./F1 |
|---|---|
| Gemini 1.0 Pro  | 1.0/1.0 |
| GPT-4 Turbo  | 0.64/0.63 |
| Claude 3 Sonnet  | 0.57/0.53 |
| Llama 3 70B | 0.58/0.55 |
| Qwen2-72B  | 0.53/0.50 |
| BERTurk | 0.66/0.64 |

For a critique of the results, misclassified instances and more please consult to the [research paper]().

## Citation
Coming soon!!