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Consumer Product Reviews and Sentiment Analysis
Reviews & Ratings
Tags and Keywords
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"No reviews yet"
Free
About
This dataset contains customer reviews for various products, including details about product categories, brands, user ratings, and sentiment analysis. It is designed for applications such as sentiment classification, product recommendation systems, and the analysis of consumer behaviour. The dataset allows users to identify trends in customer satisfaction and gain insights into consumer preferences based on brand and category.
Columns
- item_category: The category identifier of the product under review.
- item_id: The unique identifier for a specific product.
- brand: The brand identifier associated with the product.
- user_id: The unique identifier of the customer who submitted the review.
- date: The date when the review was posted, typically in YYYY-MM-DD format.
- comment: The textual content of the review as provided by the user.
- rating: The numerical rating given by the user, often on a scale (e.g., 1 to 5).
- tonality: The sentiment classification of the review, indicating whether it is positive or negative.
Distribution
The data file is typically available in CSV format. The dataset comprises approximately 14,221 records. Analysis of the sentiment distribution within the dataset indicates that 84% of reviews are classified as positive, while 16% are classified as negative.
Usage
This dataset is ideally suited for several applications, including:
- Performing sentiment analysis on product reviews to gauge public opinion.
- Identifying patterns and trends in customer satisfaction over time.
- Developing and improving product recommendation systems.
- Understanding consumer preferences based on specific brands and product categories.
Coverage
The dataset covers a time range from 30th July 2009 to 25th July 2017. The data has a global regional scope. No specific demographic scope is detailed within the available information.
License
CCO
Who Can Use It
This dataset is valuable for a range of users and their specific applications:
- Data Scientists and Machine Learning Engineers: To train and evaluate sentiment analysis models, develop natural language processing (NLP) applications, and build recommendation engines.
- Marketing Professionals: To understand customer feedback, identify popular products, and assess the impact of marketing campaigns on brand perception.
- Businesses and Product Managers: To inform product development strategies, monitor customer satisfaction, and identify areas for improvement based on consumer feedback.
- Researchers: For academic studies on consumer behaviour, sentiment analysis techniques, and market trends.
Dataset Name Suggestions
- Consumer Product Reviews and Sentiment Analysis
- Customer Feedback and Ratings
- Product Review Tonality Dataset
- E-commerce Customer Insights
- Global Product Review Data
Attributes
Original Data Source: 🏬🛍️😀 Consumer Sentiments and Ratings
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