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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:
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:
review
text to identify recurring technical issues, crashes, or bugs, allowing developers to prioritize fixes based on user impact.review
text to inform future product roadmap decisions and develop features users actively desire.review
field.rating
and sentiment
after new app updates to assess the effectiveness of bug fixes or new features.Market Research & Competitive Intelligence:
Marketing & App Store Optimization (ASO):
review
and title
fields to gauge overall user satisfaction, pinpoint specific positive and negative aspects, and track sentiment shifts over time.rating
trends and identify critical reviews quickly to facilitate timely responses and proactive customer engagement.Academic & Data Science Research:
review
and title
fields are excellent for training and testing NLP models for sentiment analysis, topic modeling, named entity recognition, and text summarization.rating
distribution, isEdited
status, and date
to understand user engagement and feedback cycles.country
-specific reviews to understand regional differences in app perception, feature preferences, or cultural nuances in feedback.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.
Apple App Store dataset to explore detailed information on app popularity, user feedback, and monetization features. Popular use cases include market trend analysis, app performance evaluation, and consumer behavior insights in the mobile app ecosystem.
Use our Apple App Store dataset to gain comprehensive insights into the mobile app ecosystem, including app popularity, user ratings, monetization features, and user feedback. This dataset covers various aspects of apps, such as descriptions, categories, and download metrics, offering a full picture of app performance and trends.
Tailored for marketers, developers, and industry analysts, this dataset allows you to track market trends, identify emerging apps, and refine promotional strategies. Whether you're optimizing app development, analyzing competitive landscapes, or forecasting market opportunities, the Apple App Store dataset is an essential tool for making data-driven decisions in the ever-evolving mobile app industry.
This dataset is versatile and can be used for various applications: - Market Analysis: Analyze app pricing strategies, monetization features, and category distribution to understand market trends and opportunities in the App Store. This can help developers and businesses make informed decisions about their app development and pricing strategies. - User Experience Research: Study the relationship between app ratings, number of reviews, and app features to understand what drives user satisfaction. The detailed review data and ratings can provide insights into user preferences and pain points. - Competitive Intelligence: Track and analyze apps within specific categories, comparing features, pricing, and user engagement metrics to identify successful patterns and market gaps. Particularly useful for developers planning new apps or improving existing ones. - Performance Prediction: Build predictive models using features like app size, category, pricing, and language support to forecast potential app success metrics. This can help in making data-driven decisions during app development. - Localization Strategy: Analyze the languages supported and regional performance to inform decisions about app localization and international market expansion.
CUSTOM Please review the respective licenses below: 1. Data Provider's License - Bright Data Master Service Agreement
From the selected regions, the ranking by population share with internet access is led by Bahrain with 98 percent and is followed by the United Arab Emirates (98 percent). In contrast, the ranking is trailed by Uganda with 13.62 percent, recording a difference of 84.38 percentage points to Bahrain. The penetration rate refers to the share of the total population having access to the internet via any means. The shown figures have been derived from survey data that has been processed to estimate missing demographics.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than 150 countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).
This dataset comprises 100,000 reviews of the ChatGPT app, sourced from the Google Play Store. It includes diverse feedback from users in ten countries, offering valuable insights into user experiences and app performance. The dataset is ideal for natural language processing tasks, sentiment analysis, and user feedback studies.
Dataset Features Name: Username of the reviewer. Rating: The star rating given by the user, ranging from 1 to 5. Comment: The textual content of the review. Date: The date the review was posted. Country: The country code of the reviewer (limited to 'us', 'gb', 'ca', 'au', 'in', 'jp', 'de', 'fr', 'kr', 'br'). Thumbs Up: Number of thumbs up (likes) the review received. Review ID:Unique identifier for the review. App Version: The version of the app being reviewed. Potential Use Cases Sentiment Analysis: Evaluate user sentiment to gauge satisfaction and identify improvement areas. Natural Language Processing (NLP): Apply NLP techniques for text classification, summarization, and keyword extraction. Trend Analysis: Monitor changes in user feedback over time and across different app versions. Market Research: Analyze user preferences and issues across various regions and demographics. Acknowledgements This dataset is collected from publicly available user reviews on the Google Play Store. We extend our gratitude to the users who provided their feedback, which facilitates further research and development.
CC BY-NC-SA
Original Data Source: ChatGPT Reviews
How much time do people spend on social media? As of 2025, the average daily social media usage of internet users worldwide amounted to 141 minutes per day, down from 143 minutes in the previous year. Currently, the country with the most time spent on social media per day is Brazil, with online users spending an average of 3 hours and 49 minutes on social media each day. In comparison, the daily time spent with social media in the U.S. was just 2 hours and 16 minutes. Global social media usageCurrently, the global social network penetration rate is 62.3 percent. Northern Europe had an 81.7 percent social media penetration rate, topping the ranking of global social media usage by region. Eastern and Middle Africa closed the ranking with 10.1 and 9.6 percent usage reach, respectively. People access social media for a variety of reasons. Users like to find funny or entertaining content and enjoy sharing photos and videos with friends, but mainly use social media to stay in touch with current events friends. Global impact of social mediaSocial media has a wide-reaching and significant impact on not only online activities but also offline behavior and life in general. During a global online user survey in February 2019, a significant share of respondents stated that social media had increased their access to information, ease of communication, and freedom of expression. On the flip side, respondents also felt that social media had worsened their personal privacy, increased a polarization in politics and heightened everyday distractions.
This statistic shows a ranking of the estimated number of Whatsapp users in 2020 in Africa, differentiated by country. The user numbers have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than 150 countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).
This statistic shows a ranking of the estimated number of Instagram users in 2020 in Africa, differentiated by country. The user numbers have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than 150 countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).
This statistic shows a ranking of the estimated number of Youtube users in 2020 in Africa, differentiated by country. The user numbers have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than 150 countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).
This statistic shows a ranking of the estimated number of LinkedIn users in 2020 in Africa, differentiated by country. The user numbers have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than 150 countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).
This statistic shows a ranking of the estimated number of Twitter users in 2020 in Africa, differentiated by country. The user numbers have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than 150 countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).
This statistic shows a ranking of the estimated number of Reddit users in 2020 in Africa, differentiated by country. The user numbers have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once. Reddit users encompass both users that are logged in and those that are not.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than 150 countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).
This statistic shows a ranking of the estimated number of Pinterest users in 2020 in Africa, differentiated by country. The user numbers have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than 150 countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).
This statistic shows a ranking of the estimated number of Facebook users in 2020 in Africa, differentiated by country. The user numbers have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than 150 countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).
This statistic shows a ranking of the estimated average internet connection speed in 2020 in Africa, differentiated by country. The speed refers to a weighted average of fixed- and mobile broadband connection speeds. Fixed-broadband connections are weighted with the household size in the selected region to account for the fact that these connections are usually shared by multiple household members.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than 150 countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).
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https://crawlfeeds.com/privacy_policyhttps://crawlfeeds.com/privacy_policy
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:
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:
review
text to identify recurring technical issues, crashes, or bugs, allowing developers to prioritize fixes based on user impact.review
text to inform future product roadmap decisions and develop features users actively desire.review
field.rating
and sentiment
after new app updates to assess the effectiveness of bug fixes or new features.Market Research & Competitive Intelligence:
Marketing & App Store Optimization (ASO):
review
and title
fields to gauge overall user satisfaction, pinpoint specific positive and negative aspects, and track sentiment shifts over time.rating
trends and identify critical reviews quickly to facilitate timely responses and proactive customer engagement.Academic & Data Science Research:
review
and title
fields are excellent for training and testing NLP models for sentiment analysis, topic modeling, named entity recognition, and text summarization.rating
distribution, isEdited
status, and date
to understand user engagement and feedback cycles.country
-specific reviews to understand regional differences in app perception, feature preferences, or cultural nuances in feedback.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.