Women's E-Commerce Clothing Reviews
This dataset contains customer reviews of apparel products, including free text, ratings, recommendations, ages, and other information. It allows you to work on NLP issues, opinion classification, or even the analysis of purchasing behavior.
Description
The dataset Women's E-Commerce Clothing Reviews contains 23,486 reviews written by customers on clothes purchased online. Each line corresponds to customer feedback including information such as the rating awarded, age, summary, review text, and product recommendation information. All data is anonymized, with brand references removed.
What is this dataset for?
- Train models for sentiment analysis or review classification
- Conduct customer experience studies by age or product category
- Explore NLP approaches like BERT, TF-IDF, or Word2Vec on real data
Can it be enriched or improved?
Yes, for example, you can cross these reviews with external data (price, stock, returns), generate additional labels (positive, neutral, negative) from the text, or even translate and adapt the data to other languages for multilingual use. The addition of lexical preprocessing also improves model performance.
🔎 In summary
🧠 Recommended for
- Marketing analysts
- NLP specialists
- Recommendation system developers
🔧 Compatible tools
- Hugging Face Transformers
- Scikit-learn
- SpacY
- NLTK
💡 Tip
To improve the detection of feelings, combine the binary recommendation score with the semantic analysis of the text.
Frequently Asked Questions
Can this dataset be used to train a recommendation model?
Yes, rating and recommendation variables and product characteristics make it possible to model suggestion systems.
Does the text of the reviews contain brand names or company names?
No, all mentions have been anonymized and replaced by “retailer”.
Is it suitable for multilingual analysis?
No, the dataset is in English only, but it can be translated or enriched for multilingual analysis.
