Dark Mode
Customer Review Analytics Dataset
Reviews & Ratings
Tags and Keywords
Trusted By
"No reviews yet"
Free
About
Welcome to the Women’s Clothing E-Commerce Reviews dataset, a collection of customer reviews designed for deep textual analysis. This dataset offers a rich environment for parsing review text through its multiple dimensions, making it suitable for a variety of analytical tasks. Given that this is real commercial data, all identifying company references within the review text and body have been meticulously anonymised and replaced with "retailer".
Columns
- Clothing ID: An integer categorical variable that uniquely refers to the specific clothing item being reviewed [1].
- Age: A positive integer variable indicating the age of the reviewer [1, 2].
- Title: A string variable capturing the title of the customer's review [1, 2].
- Review Text: A string variable containing the main body of the customer's review [1, 2].
- Rating: A positive ordinal integer variable representing the product score assigned by the customer, ranging from 1 (Worst) to 5 (Best) [1, 2].
- Recommended IND: A binary variable indicating whether the customer recommends the product (1 for recommended, 0 for not recommended) [1, 2].
- Positive Feedback Count: A positive integer documenting the number of other customers who found this review helpful or positive [1, 2].
- Division Name: A categorical name detailing the high-level division of the product [1, 2].
- Department Name: A categorical name specifying the department to which the product belongs [1, 2].
- Class Name: A categorical name indicating the product's specific class [1].
Distribution
This dataset comprises 23,486 rows and 10 distinct feature variables [1]. Each row meticulously corresponds to a single customer review [1]. The data file is typically presented in CSV format [3]. A sample file is intended to be updated separately to the platform [3].
Usage
This dataset is ideally suited for tasks involving sentiment analysis, natural language processing (NLP), and classification [1]. Potential applications include developing robust product recommendation systems, identifying emerging trends in customer feedback, and conducting in-depth market research into consumer preferences and satisfaction within the e-commerce fashion sector.
Coverage
The dataset is globally relevant [4], encompassing a wide range of customer reviews from an e-commerce context. While specific time ranges are not detailed, it includes demographic information such as the reviewer's age [1, 2]. The commercial nature of the data means it has been anonymised to protect privacy [1].
License
CC0
Who Can Use It
This dataset is highly beneficial for data scientists, machine learning engineers, and natural language processing specialists aiming to build predictive models or perform textual analysis [1]. Furthermore, market researchers and business analysts can leverage this data to gain insights into customer behaviour, product performance, and overall market sentiment within the clothing e-commerce domain.
Dataset Name Suggestions
- Women's E-Commerce Clothing Reviews [1]
- Online Fashion Customer Feedback
- Apparel Review Sentiment Data
- E-commerce Clothing Product Ratings
- Customer Review Analytics Dataset
Attributes
Original Data Source: 👗 Women's E-Commerce Clothing Reviews
QUALITY