metadata

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

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 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.

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!!