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Consumer Clothing Reviews for Multilabel Classification
Product Reviews & Feedback
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About
Consumer reviews pertaining to clothing products provide a vast collection of feedback for multilabel classification research. This material enables the exploration of various dimensions of apparel, offering diverse perspectives and opinions to assist in the development of robust machine learning models. By meticulously annotating individual entries, the data supports the prediction of multiple aspects of clothing items, contributing to a deeper understanding of consumer sentiment within the fashion industry.
Columns
- Title: The headline of the user review, which is 92% valid. The most frequent entry is "Perfect".
- Review: The primary body of feedback provided by the consumer, showing 98% validity. "Love it" is the most common review text.
- Cons_rating: A numerical consumer rating on a scale of 1 to 5. It has 100% validity with a mean score of 4.1.
- Cloth_class: The category of the garment, such as Dresses or Blouses. This field is 100% valid and contains 24 unique classes.
- Materials: Labels related to fabric and material composition, valid for 12% of the entries.
- Construction: Annotations regarding the assembly and build of the clothing, with 12% validity.
- Color: Labels identifying the colour of the product, present in 12% of the records.
- Finishing: Details concerning the final touches and finishing of the item, valid for 12% of the dataset.
- Durability: Annotations reflecting the longevity and wear-resistance of the product, showing 12% validity.
Distribution
The data is provided in a CSV file named
data_amazon.xlsx - Sheet1.csv with a file size of 12.55 MB. It contains approximately 49,300 records. While core columns like ratings and classes are fully populated, the specific attribute labels (Materials, Color, etc.) are available for roughly 12% of the total entries, with the remaining 88% marked as missing. The expected update frequency is annually.Usage
This resource is ideal for multilabel classification research and the training of machine learning algorithms. It can be used to build models that predict multiple product attributes simultaneously from raw text. Additionally, it serves as a foundation for exploratory data analysis, statistical analysis of consumer trends, and sentiment analysis within the retail and fashion sectors.
Coverage
The scope includes clothing product reviews collected in 2023. It captures a wide range of consumer perspectives across 24 different clothing classes. The data represents a variety of opinions and diverse feedback styles found in online retail environments.
License
CC0: Public Domain
Who Can Use It
Machine learning researchers and data scientists can utilise this material to advance multilabel classification techniques. Fashion industry analysts and retail strategists can apply the data to understand consumer preferences and improve product descriptions. It is also a valuable asset for students and academics performing statistical analysis or natural language processing projects.
Dataset Name Suggestions
- Consumer Clothing Reviews for Multilabel Classification
- Fashion Product Sentiment and Attribute Dataset 2023
- Multidimensional Apparel Consumer Feedback Corpus
Attributes
Original Data Source:Consumer Clothing Reviews for Multilabel Classification
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