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E-commerce Headphone Sentiment Dataset
Reviews & Ratings
Tags and Keywords
Trusted By
"No reviews yet"
Free
About
This dataset contains a collection of Amazon headphone reviews, processed for sentiment analysis. It is a small subset intended to assist in understanding customer opinions and evaluating product perceptions. The data supports analysis of review usefulness, factors influencing helpfulness, and the detection of atypical or potentially misleading reviews.
Columns
- Customer_Name: The name of the customer who provided the review.
- REVIEW_TITLE: A short summary or title of the customer's review.
- Color: The colour of the headphone product being reviewed.
- REVIEW_DATE: The specific date when the customer submitted their review.
- COMMENTS: Detailed comments from the customer expressing their feelings or observations about the product.
- RATINGS: The customer's rating for the product, given on a scale of 1 to 5 stars.
Distribution
This dataset is typically provided in a CSV file format. It comprises approximately 1,500 individual reviews. The structure includes 6 distinct columns, making it readily available for analytical tasks.
Usage
This dataset is ideally suited for:
- Conducting sentiment analysis on product reviews.
- Exploring factors that influence the perceived helpfulness of a review.
- Identifying unusual review patterns or potential outliers.
- Applications in Natural Language Processing (NLP), text mining, and exploratory data analysis.
Coverage
The data spans a time range from 28 May 2021 to 13 June 2022. It covers various customer names, including "Amazon Customer" and "Rahul", alongside a large proportion of "Other" customers. Product colours predominantly include "White" and "Black". The ratings are distributed across several ranges, from 1.00-1.40 up to 4.60-5.00. The geographical scope of the data is global.
License
CCO
Who Can Use It
This dataset is beneficial for data scientists, machine learning engineers, business analysts, and researchers interested in:
- Developing sentiment analysis models.
- Understanding consumer feedback and product performance.
- Performing text-based data analysis.
- Exploring e-commerce review patterns.
Dataset Name Suggestions
- Amazon Headphone Reviews for Sentiment Analysis
- Headphone Customer Review Data
- E-commerce Headphone Sentiment Dataset
- Product Review Analysis Data (Headphones)
Attributes
Original Data Source: HEADPHONE DATASET REVIEW ANALYSIS
QUALITY