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Customer Feedback Analysis Collection
Data Science and Analytics
Tags and Keywords
Trusted By
"No reviews yet"
Free
About
This dataset comprises a collection of product reviews gathered from prominent e-commerce platforms, including Hepsiburada, Trendyol, and N11. It provides a valuable resource for various data science and analytics tasks, offering insights into customer feedback and sentiment towards diverse products. The dataset is particularly well-suited for developing and evaluating models for classification, text mining, and natural language processing applications.
Columns
- Metin (Review Text): This column contains the actual text of the product review comments.
- Durum (Sentiment Status): This column indicates the sentiment of the review, categorised as 0 for negative, 1 for positive, and 2 for neutral.
Distribution
The dataset is provided in CSV UTF-8 format. It contains a total of 15,170 reviews. Within this collection, there are 6,799 positive reviews, 6,978 negative reviews, and 1,393 neutral reviews, providing a varied distribution of sentiment.
Usage
This dataset is ideal for:
- Sentiment Analysis: Building and training models to classify product review sentiment.
- Text Classification: Developing algorithms to categorise text based on expressed opinions.
- Natural Language Processing (NLP) Research: Exploring various NLP techniques such as topic modelling, named entity recognition, and language understanding in the context of e-commerce feedback.
- Customer Feedback Analysis: Gaining insights into customer satisfaction and product performance.
Coverage
The data originates from various e-commerce platforms, offering a global scope in terms of its potential applicability. The dataset was listed on 8th June 2025. Specific demographic or precise temporal ranges for the collected reviews are not detailed in the available information, though it pertains to online consumer product commentary.
License
CCO
Who Can Use It
This dataset is suitable for:
- Data Scientists: For machine learning projects focused on text analysis and classification.
- Machine Learning Engineers: To train and test sentiment analysis models.
- Academic Researchers: For studies in computational linguistics, natural language processing, and e-commerce analytics.
- Businesses: To understand customer opinions and improve product offerings or services.
Dataset Name Suggestions
- E-commerce Product Review Sentiment
- Turkish Online Review Dataset
- Customer Feedback Analysis Collection
- Multi-Platform Product Opinions
Attributes
Original Data Source: E-Ticaret Ürün Yorumları
QUALITY