This is a fictional dataset created for the purpose of learning and practicing data analysis, visualization, and machine learning.

It includes details of 300 fictional mobile apps with fields like:

App Name

Category

Rating

Reviews Count

App Size (MB)

Installs

Price (USD)

This dataset is ideal for beginners who want to:

Practice exploratory data analysis (EDA)

Build dashboards and visualizations

Train simple ML models on app performance

Unlock the power of user feedback with our iOS App Store Reviews Dataset, a comprehensive collection of reviews from thousands of apps across various categories. This robust App Store dataset includes essential details such as app names, ratings, user comments, timestamps, and more, offering valuable insights into user experiences and preferences.

Perfect for app developers, marketers, and data analysts, this dataset allows you to conduct sentiment analysis, monitor app performance, and identify trends in user behavior. By leveraging the iOS App Store Reviews Dataset, you can refine app features, optimize marketing strategies, and elevate user satisfaction.

Whether you’re tracking mobile app trends, analyzing specific app categories, or developing data-driven strategies, this App Store dataset is an indispensable tool. Download the iOS App Store Reviews Dataset today or contact us for custom datasets tailored to your unique project requirements.

Ready to take your app insights to the next level? Get the iOS App Store Reviews Dataset now or explore our custom data solutions to meet your needs.

This dataset encompasses a wide-ranging collection of Google Play applications, providing a holistic view of the diverse ecosystem within the platform. It includes information on various attributes such as the title, developer, monetization features, images, app descriptions, data safety measures, user ratings, number of reviews, star rating distributions, user feedback, recent updates, related applications by the same developer, content ratings, estimated downloads, and timestamps. By aggregating this data, the dataset offers researchers, developers, and analysts an extensive resource to explore and analyze trends, patterns, and dynamics within the Google Play Store. Researchers can utilize this dataset to conduct comprehensive studies on user behavior, market trends, and the impact of various factors on app success. Developers can leverage the insights derived from this dataset to inform their app development strategies, improve user engagement, and optimize monetization techniques. Analysts can employ the dataset to identify emerging trends, assess the performance of different categories of applications, and gain valuable insights into consumer preferences. Overall, this dataset serves as a valuable tool for understanding the broader landscape of the Google Play Store and unlocking actionable insights for various stakeholders in the mobile app industry.

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:

1. 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.

2. 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.

3. 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.

4. 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 and sentiment after new app updates to assess the effectiveness of bug fixes or new features.

• 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 and title 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.

• Academic & Data Science Research:

  • Natural Language Processing (NLP): The review and title 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, and date 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.

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.

Context

While many public datasets (on Kaggle and the like) provide Apple App Store data, few counterpart datasets are available for Google Play Store apps anywhere on the web. On digging deeper, I discovered that the iTunes App Store page deploys a nicely indexed appendix-like structure to allow for simple and easy web scraping. On the other hand, Google Play Store uses sophisticated modern-day techniques (like dynamic page load) using JQuery making scraping more challenging.

Content

• There are 13 features in the dataset, and each feature indicates some details of Google application name, category, rating, reviews, size, installs, type, price, content rating genres, last updated, current version and Android version.
• App: The application name.
• Category: The category the app belongs to.
• Rating: Overall user rating of the app.
• Reviews: Number of user reviews for the app.
• Size: The size of the app.
• Installs: Number of user installs for the app.
• Type: Either "Paid" or "Free".
• Price: The price of the app.
• Content Rating: The age group the app is targeted at - "Children" / "Mature 21+" / "Adult".
• Genres: Possibly multiple genres the app belongs to.
• Last Updated: The date the app was last updated.
• Current Ver: The current version of the app.
• Android Ver: The Android version is needed for this app.

AI-powered analysis of 15783+ Shopify apps and 709376+ real user reviews, providing comprehensive app market insights and opportunity identification services

Unlock valuable insights with the Google Play Store Android Apps Dataset in CSV format, featuring detailed information on over thousands of Android apps available on the Google Play Store. This comprehensive dataset includes key attributes such as App Name, App Logo, Category, Description, Average Rating, Ratings Count, In-app Purchases, Operating System, Company, Content Rating, Images, Email, Additional Information, and more.

Perfect for market researchers, data scientists, app developers, and analysts, this dataset allows for deep analysis of app performance, user preferences, and industry trends. With data on app descriptions, content ratings, in-app purchases, and company information, you can track trends in the mobile app market, evaluate user satisfaction, and conduct competitive analysis.

The dataset is ideal for businesses looking to optimize app strategies, enhance user experience, and improve app performance based on real user feedback. Easily import the data into your favorite analysis tools to gain actionable insights for your app development or research.

With regularly updated data scraped directly from the Google Play Store, the Google Play Store Android Apps Dataset is an invaluable resource for anyone looking to explore trends, track performance, or enhance their app strategies.

This dataset contains fictional reviews from a hypothetical mobile application, generated for demo purposes in various projects. The reviews include detailed feedback from users across different countries and platforms, with additional attributes such as star ratings, like/dislike counts, and issue flags. The data was later used as an input for a large language model (LLM) to generate labeled outputs, which are included in a separate dataset named labeled_app_store_reviews. This labeled dataset can be used for machine learning tasks such as sentiment analysis, text classification, or even A/B testing simulations.

- About Dataset

• Description
  The Data Set was downloaded from Kaggle, from the following link

• Context
  Google PlayStore Android App Data. (2.3 Million+ App Data) Backup repo: https://github.com/gauthamp10/Google-Playstore-Dataset

• Content
  I've collected the data with the help of Python script (Scrapy) running on a cloud vm instance. The data was collected in the month of june 2025.

  Also checkout:

  Apple AppStore Apps dataset: https://www.kaggle.com/gauthamp10/apple-appstore-apps Android App Permission dataset: https://www.kaggle.com/gauthamp10/app-permissions-android

• Acknowledgements
  I couldn't have build this dataset without the help of Github Education and switched to facundoolano/google-play-scraper for sane reasons

• Inspiration
  Took inspiration from: https://www.kaggle.com/lava18/google-play-store-apps to build a big database for students and researchers.

Snapshot img

AI Training Dataset Market Size 2025-2029

The ai training dataset market size is valued to increase by USD 7.33 billion, at a CAGR of 29% from 2024 to 2029. Proliferation and increasing complexity of foundational AI models will drive the ai training dataset market.

Market Insights

North America dominated the market and accounted for a 36% growth during the 2025-2029.
By Service Type - Text segment was valued at USD 742.60 billion in 2023
By Deployment - On-premises segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 479.81 million 
Market Future Opportunities 2024: USD 7334.90 million
CAGR from 2024 to 2029 : 29%

Market Summary

The market is experiencing significant growth as businesses increasingly rely on artificial intelligence (AI) to optimize operations, enhance customer experiences, and drive innovation. The proliferation and increasing complexity of foundational AI models necessitate large, high-quality datasets for effective training and improvement. This shift from data quantity to data quality and curation is a key trend in the market. Navigating data privacy, security, and copyright complexities, however, poses a significant challenge. Businesses must ensure that their datasets are ethically sourced, anonymized, and securely stored to mitigate risks and maintain compliance. For instance, in the supply chain optimization sector, companies use AI models to predict demand, optimize inventory levels, and improve logistics. Access to accurate and up-to-date training datasets is essential for these applications to function efficiently and effectively. Despite these challenges, the benefits of AI and the need for high-quality training datasets continue to drive market growth. The potential applications of AI are vast and varied, from healthcare and finance to manufacturing and transportation. As businesses continue to explore the possibilities of AI, the demand for curated, reliable, and secure training datasets will only increase.

What will be the size of the AI Training Dataset Market during the forecast period?

Get Key Insights on Market Forecast (PDF) Request Free SampleThe market continues to evolve, with businesses increasingly recognizing the importance of high-quality datasets for developing and refining artificial intelligence models. According to recent studies, the use of AI in various industries is projected to grow by over 40% in the next five years, creating a significant demand for training datasets. This trend is particularly relevant for boardrooms, as companies grapple with compliance requirements, budgeting decisions, and product strategy. Moreover, the importance of data labeling, feature selection, and imbalanced data handling in model performance cannot be overstated. For instance, a mislabeled dataset can lead to biased and inaccurate models, potentially resulting in costly errors. Similarly, effective feature selection algorithms can significantly improve model accuracy and reduce computational resources. Despite these challenges, advances in model compression methods, dataset scalability, and data lineage tracking are helping to address some of the most pressing issues in the market. For example, model compression techniques can reduce the size of models, making them more efficient and easier to deploy. Similarly, data lineage tracking can help ensure data consistency and improve model interpretability. In conclusion, the market is a critical component of the broader AI ecosystem, with significant implications for businesses across industries. By focusing on data quality, effective labeling, and advanced techniques for handling imbalanced data and improving model performance, organizations can stay ahead of the curve and unlock the full potential of AI.

Unpacking the AI Training Dataset Market Landscape

In the realm of artificial intelligence (AI), the significance of high-quality training datasets is indisputable. Businesses harnessing AI technologies invest substantially in acquiring and managing these datasets to ensure model robustness and accuracy. According to recent studies, up to 80% of machine learning projects fail due to insufficient or poor-quality data. Conversely, organizations that effectively manage their training data experience an average ROI improvement of 15% through cost reduction and enhanced model performance.

Distributed computing systems and high-performance computing facilitate the processing of vast datasets, enabling businesses to train models at scale. Data security protocols and privacy preservation techniques are crucial to protect sensitive information within these datasets. Reinforcement learning models and supervised learning models each have their unique applications, with the former demonstrating a 30% faster convergence rate in certain use cases.

Data annot

Video Dataset Market Outlook

As per our latest research, the global Video Dataset Market size reached USD 2.3 billion in 2024 and is expected to grow at a robust CAGR of 21.6% during the forecast period, attaining a market size of USD 15.7 billion by 2033. The marketÂ’s growth is underpinned by the surging demand for high-quality, annotated video data to power artificial intelligence (AI) and machine learning (ML) models across diverse sectors. This expansion is further fueled by technological advancements in computer vision, deep learning, and the proliferation of smart devices that generate massive volumes of video content globally.

The primary growth driver for the video dataset market is the exponential increase in the adoption of AI-driven applications across industries such as automotive, healthcare, retail, and surveillance. The need for large-scale, well-annotated video datasets is crucial for training and validating AI models, particularly in applications like autonomous vehicles, facial recognition, and smart surveillance systems. As organizations continue to integrate AI into their core operations, they seek comprehensive video datasets to enhance model accuracy, reduce bias, and achieve superior outcomes. The rising sophistication of deep learning algorithms, which require vast quantities of labeled video data, further accelerates the demand for curated and high-quality video datasets.

Another significant growth factor is the rapid digitization and increasing deployment of Internet of Things (IoT) devices, which are generating unprecedented volumes of video data. The proliferation of smart cameras, drones, and connected devices in both consumer and enterprise environments has led to a surge in unstructured video data, which must be effectively managed, labeled, and analyzed. The emergence of edge computing and cloud-based video processing solutions has also made it easier for organizations to leverage large video datasets for real-time analytics, predictive maintenance, and enhanced decision-making. These technological advancements are enabling new applications in sectors like healthcare, where video datasets play a pivotal role in diagnostics, remote monitoring, and telemedicine.

Furthermore, the increasing regulatory emphasis on data privacy and security has driven organizations to seek reliable and compliant video dataset providers. The need for datasets that adhere to regional data governance standards, such as GDPR in Europe and CCPA in California, is prompting market participants to invest in secure data annotation and management practices. As AI and ML applications become more pervasive, ensuring the ethical use of video data and maintaining transparency in data sourcing and labeling are becoming critical considerations for enterprises. This trend is fostering the emergence of specialized service providers who offer not just raw video data, but also end-to-end data management, annotation, and compliance solutions.

From a regional perspective, North America continues to dominate the video dataset market, driven by the presence of leading technology companies, a mature AI ecosystem, and significant investments in research and development. The region benefits from strong government support for AI innovation, a robust startup landscape, and the widespread adoption of advanced technologies in sectors such as automotive, healthcare, and retail. Europe is also witnessing substantial growth, propelled by stringent data privacy regulations and increasing adoption of AI-powered solutions across industries. Meanwhile, the Asia Pacific region is emerging as a high-growth market, fueled by rapid digital transformation, expanding internet penetration, and the rising use of video analytics in sectors like smart cities and public safety. These regional dynamics are shaping the competitive landscape and driving innovation in the global video dataset market.

Artificial Intelligence (AI) Training Dataset is a cornerstone in the development of robust AI models, particularly in the video dataset market. These datasets provide the foundational data necessary for training AI systems to recognize patterns, make predictions, and improve decision-making processes. The quality and diversity of AI training datasets are crucial, as they directly impact the performance and a

Video Dataset Market Outlook

Based on our latest research, the global video dataset market size reached USD 2.1 billion in 2024 and is projected to grow at a robust CAGR of 19.7% during the forecast period, reaching a value of USD 10.3 billion by 2033. This remarkable growth trajectory is driven by the increasing adoption of artificial intelligence and machine learning technologies, which heavily rely on high-quality video datasets for training and validation purposes. As organizations across industries seek to leverage advanced analytics and automation, the demand for comprehensive, well-annotated video datasets is accelerating rapidly, establishing the video dataset market as a critical enabler for next-generation digital solutions.

One of the primary growth factors propelling the video dataset market is the exponential rise in the deployment of computer vision applications across diverse sectors. Industries such as automotive, healthcare, retail, and security are increasingly integrating AI-powered vision systems for tasks ranging from autonomous navigation and medical diagnostics to customer behavior analysis and surveillance. The effectiveness of these systems hinges on the availability of large, diverse, and accurately labeled video datasets that can be used to train robust machine learning models. With the proliferation of video-enabled devices and sensors, the volume of raw video data has surged, further fueling the need for curated datasets that can be harnessed to unlock actionable insights and drive automation.

Another significant driver for the video dataset market is the growing emphasis on data-driven research and innovation within academic, commercial, and governmental institutions. Universities and research organizations are leveraging video datasets to advance studies in areas such as robotics, behavioral science, and smart city development. Similarly, commercial entities are utilizing these datasets to enhance product offerings, improve customer experiences, and gain a competitive edge through AI-driven solutions. Government and defense agencies are also investing in video datasets to bolster national security, surveillance, and public safety initiatives. This broad-based adoption across end-users is catalyzing the expansion of the video dataset market, as stakeholders recognize the strategic value of high-quality video data in driving technological progress and operational efficiency.

The emergence of synthetic and augmented video datasets represents a transformative trend within the market, addressing challenges related to data scarcity, privacy, and bias. Synthetic datasets, generated using advanced simulation and generative AI techniques, enable organizations to create vast amounts of labeled video data tailored to specific scenarios without the need for extensive real-world data collection. This approach not only accelerates model development but also enhances data diversity and mitigates ethical concerns associated with using sensitive or personally identifiable information. As the technology for generating and validating synthetic video data matures, its adoption is expected to further accelerate, opening new avenues for innovation and market growth.

Regionally, North America continues to dominate the video dataset market, accounting for the largest share in 2024 due to its advanced technological ecosystem, strong presence of leading AI companies, and substantial investments in research and development. However, the Asia Pacific region is witnessing the fastest growth, driven by rapid digital transformation, increasing adoption of AI in sectors like manufacturing and healthcare, and supportive government policies. Europe also represents a significant market, characterized by its focus on data privacy and regulatory compliance, which is shaping the development and utilization of video datasets across industries. These regional dynamics underscore the global nature of the video dataset market and highlight the diverse opportunities for stakeholders worldwide.

Dataset Type Analysis

The video dataset market is segmented by dataset type into labeled, unlabeled, and synthetic datasets, each serving distinct purposes and addressing unique industry requirements. Labeled video datasets are foundational for supervised learning applications, where annotated frames and sequences enable machine learning models to learn complex patterns and behaviors. The demand for labeled datasets is particularly high in sectors

Dataset Licensing for AI Training Market Outlook

According to our latest research, the global Dataset Licensing for AI Training market size reached USD 2.1 billion in 2024, with a robust CAGR of 22.4% projected through the forecast period. By 2033, the market is expected to achieve a value of USD 15.2 billion. This remarkable growth is primarily fueled by the exponential rise in demand for high-quality, diverse, and ethically sourced datasets required to train increasingly sophisticated artificial intelligence (AI) models across industries. As organizations continue to scale their AI initiatives, the need for compliant, scalable, and customizable licensing solutions has never been more critical, driving significant investments and innovation in the dataset licensing ecosystem.

A primary growth factor for the Dataset Licensing for AI Training market is the proliferation of AI applications across sectors such as healthcare, finance, automotive, and government. As AI models become more complex, their hunger for diverse and representative datasets intensifies, making data acquisition and licensing a strategic priority for enterprises. The increasing adoption of machine learning, deep learning, and generative AI technologies further amplifies the need for specialized datasets, pushing both data providers and consumers to seek flexible and secure licensing arrangements. Additionally, regulatory developments such as GDPR in Europe and similar data privacy frameworks worldwide are compelling organizations to prioritize licensed, compliant datasets over ad hoc or unlicensed data sources, further accelerating market growth.

Another significant driver is the growing sophistication of dataset licensing models themselves. Vendors are moving beyond traditional open-source or proprietary licenses, introducing hybrid, creative commons, and custom-negotiated agreements tailored to specific use cases and industries. This evolution is enabling AI developers to access a broader variety of data types—text, image, audio, video, and multimodal—while ensuring legal clarity and minimizing risk. Moreover, the rise of data marketplaces and third-party platforms is streamlining the process of dataset discovery, negotiation, and compliance monitoring, making it easier for organizations of all sizes to source and license the data they need for AI training at scale.

The surging demand for high-quality annotated datasets is also fostering partnerships between data providers, annotation service vendors, and AI developers. These collaborations are leading to the creation of bespoke datasets that cater to niche applications, such as autonomous driving, medical diagnostics, and advanced robotics. At the same time, advances in synthetic data generation and data augmentation are expanding the universe of licensable datasets, offering new avenues for licensing and monetization. As the market matures, we expect to see increased standardization, transparency, and interoperability in licensing frameworks, further lowering barriers to entry and accelerating innovation in AI model development.

Regionally, North America continues to dominate the Dataset Licensing for AI Training market, accounting for the largest share in 2024, driven by the presence of leading technology companies, robust regulatory frameworks, and a mature AI ecosystem. Europe follows closely, with significant investments in ethical AI and data governance initiatives. Asia Pacific is emerging as a high-growth region, fueled by rapid digital transformation, government-backed AI strategies, and a burgeoning startup landscape. Latin America and the Middle East & Africa are also witnessing increased adoption of licensed datasets, particularly in sectors such as healthcare and public administration, although their market shares remain comparatively smaller. This global momentum underscores the universal need for high-quality, licensed datasets as the foundation of responsible and effective AI training.

License Type Analysis

The License Type segment in the Dataset Licensing for AI Training market is characterized by a diverse range of options, including Open Source, Proprietary, Creative Commons, and Custom/Negotiated licenses. Open source licenses have long been favored by academic and research communities due to their accessibility and collaborative ethos. However, their adoption in commercial AI projects is often tempered by concerns over data provenance, usage restrictions, a

Please share your suggestions to improve my datasets further✍️

📄 Dataset Overview This dataset contains Google Play Store app reviews labeled for sentiment using a deterministic Large Language Model (LLM) classification pipeline. Each review is tagged as positive, negative, or neutral, making it ready for NLP training, benchmarking, and market insight generation.

⚙️ Data Collection & Labeling Process Source: Reviews collected from Google Play Store using the google_play_scraper library. Labeling: Reviews classified by a Hugging Face Transformers-based LLM with a strict prompt to ensure one-word output. Post-processing: Outputs normalized to the three sentiment classes.

💡 Potential Uses Fine-tuning BERT, RoBERTa, LLaMA, or other transformer models. Sentiment dashboards for product feedback monitoring. Market research on user perception trends. Benchmark dataset for text classification experiments.

Please upvote!!!!

Apple App Store Key StatisticsApps & Games in the Apple App StoreApps in the Apple App StoreGames in the Apple App StoreMost Popular Apple App Store CategoriesPaid vs Free Apps in Apple App...

This comprehensive iOS application reviews dataset contains thousands of authentic user reviews from the Apple App Store in English. The dataset provides valuable insights for app developers, marketers, and researchers studying mobile application performance and user sentiment.

Key Features:

• Real user reviews from popular iOS apps
• Star ratings from 1 to 5 stars
• Review dates and timestamps
• App store URLs and metadata
• User demographics and location data
• App version information
• Review titles and detailed feedback

Applications: Perfect for sentiment analysis, app store optimization, mobile app development research, user experience studies, and competitive analysis. This dataset enables businesses to understand user preferences, identify app improvement opportunities, and develop better mobile applications.

Data Quality: All reviews are genuine user feedback collected from the official Apple App Store, ensuring authenticity and reliability for research and business intelligence purposes. The dataset covers various app categories including fitness, shopping, education, entertainment, and productivity applications.

iOS App Reviews Dataset

Unlock the potential of user feedback with our extensive iOS App Reviews Dataset. This dataset contains detailed reviews from a wide range of iOS applications, providing invaluable insights for developers, researchers, and marketers.

Key Features:

• Comprehensive Review Data: Thousands of user reviews covering various iOS apps.
• Sentiment Analysis Ready: Perfect for sentiment analysis, enabling you to understand user satisfaction and areas for improvement.
• Market Research: Gain insights into market trends and user preferences.
• App Performance Evaluation: Evaluate the performance and reception of different apps.
• Structured Data: Easily accessible and well-organized data for efficient analysis.

Last crawled at: 29 march 2021

Individual column percentage

Countries covered: 102

tr, my, sa, mx, au, us, lb, fr, cz, om, gb, ar, br, se, pe, cl, ph, co, es, cr, no, it, de, pl, be, za, ru, tw, cn, ng, kr, ca, ua, jp, sv, vn, nl, in, do, ro, hu, ch, at, sg, th, id, ae, pa, dk, mo, gr, ec, hk, gt, pt, pk, nz, kw, bo, kz, lu, gh, ie, ve, eg, ke, il, qa, bg, hr, cy, fi, lt, dz, by, kh, lv, iq, lk, uz, uy, az, py, sk, mz, rs, mt, bh, ao, bb, ni, mg, ly, si, tn, ma, ee, mm, ge, ye, bm, af

This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.

The global Artificial Intelligence (AI) Training Dataset market is projected to reach $1605.2 million by 2033, exhibiting a CAGR of 9.4% from 2025 to 2033. The surge in demand for AI training datasets is driven by the increasing adoption of AI and machine learning technologies in various industries such as healthcare, financial services, and manufacturing. Moreover, the growing need for reliable and high-quality data for training AI models is further fueling the market growth. Key market trends include the increasing adoption of cloud-based AI training datasets, the emergence of synthetic data generation, and the growing focus on data privacy and security. The market is segmented by type (image classification dataset, voice recognition dataset, natural language processing dataset, object detection dataset, and others) and application (smart campus, smart medical, autopilot, smart home, and others). North America is the largest regional market, followed by Europe and Asia Pacific. Key companies operating in the market include Appen, Speechocean, TELUS International, Summa Linguae Technologies, and Scale AI. Artificial Intelligence (AI) training datasets are critical for developing and deploying AI models. These datasets provide the data that AI models need to learn, and the quality of the data directly impacts the performance of the model. The AI training dataset market landscape is complex, with many different providers offering datasets for a variety of applications. The market is also rapidly evolving, as new technologies and techniques are developed for collecting, labeling, and managing AI training data.

AI Dataset Management Market Outlook

According to our latest research, the AI Dataset Management market size reached USD 1.82 billion in 2024, reflecting robust momentum driven by the increasing adoption of artificial intelligence across diverse industries. The market is projected to grow at a CAGR of 27.6% from 2025 to 2033, reaching a forecasted value of USD 14.35 billion by 2033. This rapid expansion is propelled by the surging need for high-quality, well-managed datasets to fuel AI and machine learning models, coupled with the proliferation of data-intensive applications in sectors such as healthcare, finance, and retail. As per our latest research, the market’s upward trajectory is further supported by advancements in data labeling, annotation tools, and stringent regulatory requirements for data governance.

One of the primary growth factors for the AI Dataset Management market is the exponential increase in data generation from connected devices, social media platforms, IoT sensors, and enterprise applications. Organizations are increasingly recognizing that the quality and integrity of their AI models are directly tied to the quality of the underlying datasets. As a result, there is a growing demand for sophisticated dataset management solutions that can automate data collection, cleansing, labeling, and augmentation. These solutions not only streamline the AI development lifecycle but also ensure compliance with evolving data privacy regulations such as GDPR and CCPA. Furthermore, the integration of advanced technologies like natural language processing and computer vision into dataset management platforms is enhancing their ability to handle complex, unstructured data, further stimulating market growth.

Another significant driver is the expanding application of AI across verticals such as healthcare, BFSI, retail, automotive, and government. In healthcare, for instance, the need for annotated medical images and patient records is spurring investment in specialized dataset management tools. Similarly, financial institutions are leveraging AI dataset management to detect fraud, manage risk, and personalize customer experiences. The retail and e-commerce sector is utilizing these solutions for customer segmentation, demand forecasting, and inventory optimization. This cross-industry adoption is creating a fertile environment for both established players and innovative startups to introduce tailored offerings that address the unique data challenges of each sector. As a result, the market is witnessing a wave of product innovation, strategic partnerships, and mergers and acquisitions aimed at expanding capabilities and geographic reach.

Additionally, the shift towards cloud-based deployment models is accelerating the adoption of AI dataset management solutions, especially among small and medium enterprises (SMEs) that require scalable, cost-effective tools. Cloud platforms offer the flexibility to store, process, and manage large volumes of data without significant upfront investment in IT infrastructure. This democratization of AI dataset management is leveling the playing field, enabling organizations of all sizes to harness the power of AI for competitive advantage. Moreover, the emergence of open-source dataset management frameworks and APIs is lowering barriers to entry, fostering a vibrant ecosystem of developers, researchers, and data scientists. These trends are expected to sustain the market’s double-digit growth over the forecast period.

Regionally, North America continues to dominate the AI Dataset Management market, accounting for the largest revenue share in 2024, thanks to its advanced digital infrastructure, high AI adoption rates, and concentration of leading technology vendors. However, Asia Pacific is emerging as the fastest-growing region, driven by rapid digital transformation, government initiatives supporting AI research, and a burgeoning base of tech-savvy enterprises. Europe is also making significant strides, particularly in sectors such as automotive and healthcare, where stringent data protection regulations are fueling demand for robust dataset management solutions. Latin America and the Middle East & Africa are gradually catching up, with increasing investments in AI and digitalization initiatives. Overall, the regional outlook remains highly optimistic, with each geography presenting unique growth opportunities and challenges for market participants.

Component Analysis

The AI Dataset