Ratings and reviews dataset from apps
This dataset offers a focused and invaluable window into user perceptions and experiences with applications listed on the Apple App Store. It is a vital resource for app developers, product managers, market analysts, and anyone seeking to understand the direct voice of the customer in the dynamic mobile app ecosystem.
Dataset Specifications:
- Investment: $45.0
- Status: Published and immediately available.
- Category: Ratings and Reviews Data
- Format: Compressed ZIP archive containing JSON files, ensuring easy integration into your analytical tools and platforms.
- Volume: Comprises 10,000 unique app reviews, providing a robust sample for qualitative and quantitative analysis of user feedback.
- Timeliness:
Last crawled:
(This field is blank in your provided info, which means its recency is currently unknown. If this were a real product, specifying this would be critical for its value proposition.)
Richness of Detail (11 Comprehensive Fields):
Each record in this dataset provides a detailed breakdown of a single App Store review, enabling multi-dimensional analysis:
-
Review Content:
review
: The full text of the user's written feedback, crucial for Natural Language Processing (NLP) to extract themes, sentiment, and common keywords.title
: The title given to the review by the user, often summarizing their main point.isEdited
: A boolean flag indicating whether the review has been edited by the user since its initial submission. This can be important for tracking evolving sentiment or understanding user behavior.
-
Reviewer & Rating Information:
username
: The public username of the reviewer, allowing for analysis of engagement patterns from specific users (though not personally identifiable).rating
: The star rating (typically 1-5) given by the user, providing a quantifiable measure of satisfaction.
-
App & Origin Context:
app_name
: The name of the application being reviewed.app_id
: A unique identifier for the application within the App Store, enabling direct linking to app details or other datasets.country
: The country of the App Store storefront where the review was left, allowing for geographic segmentation of feedback.
-
Metadata & Timestamps:
_id
: A unique identifier for the specific review record in the dataset.crawled_at
: The timestamp indicating when this particular review record was collected by the data provider (Crawl Feeds).date
: The original date the review was posted by the user on the App Store.
Expanded Use Cases & Analytical Applications:
This dataset is a goldmine for understanding what users truly think and feel about mobile applications. Here's how it can be leveraged:
-
Product Development & Improvement:
- Bug Detection & Prioritization: Analyze negative
review
text to identify recurring technical issues, crashes, or bugs, allowing developers to prioritize fixes based on user impact. - Feature Requests & Roadmap Prioritization: Extract feature suggestions from positive and neutral
review
text to inform future product roadmap decisions and develop features users actively desire. - User Experience (UX) Enhancement: Understand pain points related to app design, navigation, and overall usability by analyzing common complaints in the
review
field. - Version Impact Analysis: If integrated with app version data, track changes in
rating
andsentiment
after new app updates to assess the effectiveness of bug fixes or new features.
- Bug Detection & Prioritization: Analyze negative
-
Market Research & Competitive Intelligence:
- Competitor Benchmarking: Analyze reviews of competitor apps (if included or combined with similar datasets) to identify their strengths, weaknesses, and user expectations within a specific app category.
- Market Gap Identification: Discover unmet user needs or features that users desire but are not adequately provided by existing apps.
- Niche Opportunities: Identify specific use cases or user segments that are underserved based on recurring feedback.
-
Marketing & App Store Optimization (ASO):
- Sentiment Analysis: Perform sentiment analysis on the
review
andtitle
fields to gauge overall user satisfaction, pinpoint specific positive and negative aspects, and track sentiment shifts over time. - Keyword Optimization: Identify frequently used keywords and phrases in reviews to optimize app store listings, improving discoverability and search ranking.
- Messaging Refinement: Understand how users describe and use the app in their own words, which can inform marketing copy and advertising campaigns.
- Reputation Management: Monitor
rating
trends and identify critical reviews quickly to facilitate timely responses and proactive customer engagement.
- Sentiment Analysis: Perform sentiment analysis on the
-
Academic & Data Science Research:
- Natural Language Processing (NLP): The
review
andtitle
fields are excellent for training and testing NLP models for sentiment analysis, topic modeling, named entity recognition, and text summarization. - User Behavior Analysis: Study patterns in
rating
distribution,isEdited
status, anddate
to understand user engagement and feedback cycles. - Cross-Country Comparisons: Analyze
country
-specific reviews to understand regional differences in app perception, feature preferences, or cultural nuances in feedback.
- Natural Language Processing (NLP): The
This App Store Reviews dataset provides a direct, unfiltered conduit to understanding user needs and ultimately driving better app performance and greater user satisfaction. Its structured format and granular detail make it an indispensable asset for data-driven decision-making in the mobile app industry.
Last crawled:
Feb 2021
Data points:
review, isEdited, title, username, rating, _id, crawled_at, date, app_name, app_id, country
Data points count:
11
Total Downloads
6 +
Total Views
478
Sample dataset:
Availability or Type:
Immediately
Delivery time:
immediately