Artificial Intelligence in Human Resource (HR) Market Outlook

According to our latest research, the global Artificial Intelligence (AI) in Human Resource (HR) market size reached USD 5.8 billion in 2024, registering a strong momentum in adoption across industries. The market is experiencing a robust CAGR of 36.2% and is forecasted to reach USD 66.4 billion by 2033. This impressive growth is primarily driven by the increasing demand for automation in HR processes, the need for data-driven insights in talent management, and the growing emphasis on employee engagement and retention strategies.

One of the principal growth factors propelling the AI in HR market is the rapid digital transformation across industries, which has led organizations to seek advanced solutions for streamlining complex HR operations. Companies are leveraging AI-powered applications to automate repetitive tasks such as resume screening, candidate shortlisting, and onboarding processes, significantly reducing administrative burdens and turnaround times. The integration of AI with existing HR management systems is enabling organizations to enhance accuracy, minimize human error, and improve the overall efficiency of HR departments. Furthermore, the ongoing shift toward remote and hybrid work models has accelerated the adoption of AI-based HR solutions, as businesses strive to maintain productivity and employee engagement in distributed work environments.

Another significant driver for the expansion of the Artificial Intelligence in Human Resource market is the increasing need for personalized employee experiences. AI technologies, such as natural language processing and machine learning, are being utilized to analyze employee feedback, predict attrition risks, and deliver tailored learning and development programs. This not only helps organizations retain top talent but also fosters a culture of continuous improvement and innovation. The ability of AI to provide actionable insights from vast datasets is transforming traditional HR practices, enabling data-driven decision-making and strategic workforce planning. Additionally, the rising focus on diversity, equity, and inclusion (DEI) initiatives is pushing enterprises to adopt AI tools that minimize bias in recruitment and performance evaluations.

The proliferation of cloud-based HR solutions is another critical factor influencing market growth. Cloud deployment offers scalability, flexibility, and cost-effectiveness, making it an attractive option for organizations of all sizes. With the increasing availability of AI-powered HR platforms on the cloud, even small and medium enterprises (SMEs) are able to access sophisticated tools that were previously only affordable for large corporations. This democratization of technology is leveling the playing field in talent acquisition and workforce management. Moreover, the integration of AI with cloud-based systems is facilitating real-time analytics, seamless collaboration, and enhanced security, further accelerating the adoption of AI in HR functions.

Regionally, North America continues to dominate the AI in HR market, accounting for the largest share due to the presence of major technology providers, high digital literacy, and early adoption of advanced HR technologies. However, Asia Pacific is emerging as the fastest-growing region, fueled by rapid economic development, increasing investments in AI research, and a burgeoning tech-savvy workforce. Europe is also witnessing significant growth, driven by stringent labor regulations and a strong focus on employee well-being. Latin America and the Middle East & Africa are gradually catching up, as organizations in these regions recognize the potential of AI to address unique workforce challenges and enhance competitiveness.

Component Analysis

The component segment of the Artificial Intelligence in Human Resource market is bifurcated into software and services, each playing a pivotal role in the ecosystem

Mental Health in Tech Survey

Understanding Employee Mental Health Needs in the Tech Industry

By Stephen Myers [source]

About this dataset

This dataset contains survey responses from individuals in the tech industry about their mental health, including questions about treatment, workplace resources, and attitudes towards discussing mental health in the workplace. Mental health is an issue that affects all people of all ages, genders and walks of life. The prevalence of these issues within the tech industry–one that places hard demands on those who work in it–is no exception. By analyzing this dataset, we can better understand how prevalent mental health issues are among those who work in the tech sector.–and what kinds of resources they rely upon to find help–so that more can be done to create a healthier working environment for all.

This dataset tracks key measures such as age, gender and country to determine overall prevalence, along with responses surrounding employee access to care options; whether mental health or physical illness are being taken as seriously by employers; whether or not anonymity is protected with regards to seeking help; and how coworkers may perceive those struggling with mental illness issues such as depression or anxiety. With an ever-evolving landscape due to new technology advancing faster than ever before – these statistics have never been more important for us to analyze if we hope remain true promoters of a healthy world inside and outside our office walls

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How to use the dataset

In this dataset you will find data on age, gender, country, and state of survey respondents in addition to numerous questions that assess an individual's mental state including: self-employment status, family history of mental illness, treatment status and access or lack thereof; how their mental health condition affects their work; number of employees at the company they work for; remote work status; tech company status; benefit information from employers such as mental health benefits and wellness program availability; anonymity protection if seeking treatment resources for substance abuse or mental health issues ; ease (or difficulty) for medical leave for a mental health condition ; whether discussing physical or medical matters with employers have negative consequences. You will also find comments from survey participants.

To use this dataset effectively: - Clean the data by removing invalid responses/duplicates/missing values - you can do this with basic Pandas commands like .dropna() , .drop_duplicates(), .replace(). - Utilize descriptive statistics such as mean and median to draw general conclusions about patterns of responses - you can do this with Pandas tools such as .groupby() and .describe(). - Run various types analyses such as mean comparisons on different kinds of variables(age vs gender), correlations between different features etc using appropriate statistical methods - use commands like Statsmodels' OLS models (.smf) , calculate z-scores , run hypothesis tests etc depending on what analysis is needed. Make sure you are aware any underlying assumptions your analysis requires beforehand !
- Visualize your results with plotting libraries like Matplotlib/Seaborn to easily interpret these findings! Use boxplots/histograms/heatmaps where appropriate depending on your question !

Research Ideas

• Using the results of this survey, you could develop targeted outreach campaigns directed at underrepresented groups that answer “No” to questions about their employers providing resources for mental health or discussing it as part of wellness programs.
• Analyzing the employee characteristics (e.g., age and gender) of those who reported negative consequences from discussing their mental health in the workplace could inform employer policies to support individuals with mental health conditions and reduce stigma and discrimination in the workplace.
• Correlating responses to questions about remote work, leave policies, and anonymity with whether or not individuals have sought treatment for a mental health condition may provide insight into which types of workplace resources are most beneficial for supporting employees dealing with these issues

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redi...

➡️ You can choose from multiple data formats, delivery frequency options, and delivery methods;

➡️ You can select raw or clean and AI-enriched datasets;

➡️ Multiple APIs designed for effortless search and enrichment (accessible using a user-friendly self-service tool);

➡️ Fresh data: daily updates, easy change tracking with dedicated data fields, and a constant flow of new data;

➡️ You get all necessary resources for evaluating our data: a free consultation, a data sample, or free credits for testing our APIs.

Coresignal's employee and company data enables you to create and improve innovative data-driven solutions and extract actionable business insights. These datasets are popular among companies from different industries, including investment, sales, and HR technology.

✅ For investors

Gain strategic business insights, enhance decision-making, and maintain algorithms that signal investment opportunities with Coresignal's global Employee Data and Company Data.

Use cases

1. Screen startups and industries showing early signs of growth
2. Identify companies hungry for the next investment
3. Check if a startup is about to reach the next maturity phase

✅ For HR tech

Coresignal's global Employee Data and Company Data enable you to build and improve AI-based talent-sourcing and other HR technology solutions.

Use cases

1. Build AI-based tools
2. Find qualified candidates
3. Enrich existing hiring data

✅ For sales tech

Companies use our large-scale datasets to improve their lead generation engines and power sales technology platforms.

Use cases

1. Extract targeted lead lists
2. Fill in the gaps in your lead data
3. Enable data-driven sales strategies

➡️ Why 400+ data-powered businesses choose Coresignal:

1. Experienced data provider (in the market since 2016);
2. Exceptional client service;
3. Responsible and secure data collection.

High-Tech Companies on NASDAQ

Market Capitalization and Performance Metrics

By [source]

About this dataset

This dataset offers an insightful look into the performance of high-tech companies listed on the NASDAQ exchange in the United States. With information pertaining to over 8,000 companies in the electronics, computers, telecommunications, and biotechnology sectors, this is an incredibly useful source of insight for researchers, traders, investors and data scientists interested in acquiring information about these firms.

The dataset includes detailed variables such as stock symbols and names to provide quick identification of individual companies along with pricing changes and percentages from the previous day’s value as well as sector and industry breakdowns for comprehensive analysis. Other metrics like market capitalization values help to assess a firm’s relative size compared to competitors while share volume data can give a glimpse into how actively traded each company is. Additionally provided numbers include earnings per share breakdowns to gauge profits along with dividend pay date symbols for yield calculation purposes as well as beta values that further inform risk levels associated with investing in particular firms within this high-tech sector. Finally this dataset also collects any potential errors found amongst such extensive scrapes of company performance data giving users valuable reassurance no sensitive areas are missed when assessing various firms on an individual basis or all together as part of an overarching system

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How to use the dataset

This dataset is invaluable for researchers, traders, investors and data scientists who want to obtain the latest information about high-tech companies listed on the NASDAQ exchange in the United States. It contains data on more than 8,000 companies from a wide range of sectors such as electronics, computers, telecommunications, biotechnology and many more. In this guide we will learn how to use this dataset effectively.

Basics: The basics of working with this dataset include understanding various columns like symbol, name, price,pricing_changes, pricing_percentage_changes,sector,industry,market_cap,share_volume,earnings_per_share. Each column is further described below: - Symbol: This column gives you the stock symbol of the company. (String) - Name: This column gives you the name of the company. (String)
- Price: The current price of each stock given by symbol is mentioned here.(Float) - Pricing Changes: This represents change in stock price from previous day.(Float) - Pricing Percentage Changes :This provides percentage change in stock prices from previous day.(Float) - Sector : It give information about sector in which company belongs .(String). - Industry : Describe industry in which company lies.(string). - Market Capitalization : Give market capitalization .(String). - Share Volume : It refers to number share traded last 24 hrs.(Integer). - Earnings Per Share : It refer to earnings per share per Stock yearly divided by Dividend Yield ,Symbol Yield and Beta .It also involves Errors related with Data Set so errors specified here proviedes details regarding same if any errors occured while collecting data set or manipulation on it.. (float/string )

Advanced Use Cases: Now that we understand what each individual feature stands for it's time to delve deeper into optimizing returns using this data set as basis for our decision making processes such as selecting right portfolio formation techniques or selecting stocks wisely contrarian investment style etc. We can do a comparison using multiple factors like Current Price followed by Price Change percentage or Earnings feedback loop which would help us identify Potentially Undervalued investments both Short Term & Long Term ones at same time and We could dive into analysis showing Relationship between Price & Volumne across Sectors and

Research Ideas

• Analyzing stock trends - The dataset enables users to make informed decisions by tracking and analyzing changes in indicators such as price, sector, industry or market capitalization trends over time.
• Exploring correlations between different factors - By exploring the correlation between different factors such as pricing changes, earning per share or beta etc., it enables us to get a better understanding of how these elements influence each other and what implications it may have on our investments

Acknowledgements

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Success.ai’s B2B Contact Data for Human Resources Professionals Worldwide empowers businesses to connect with HR leaders across the globe. With access to over 170 million verified professional profiles, this dataset includes critical contact information for key HR decision-makers in various industries. Whether you’re targeting HR directors, talent acquisition specialists, or employee relations managers, Success.ai ensures accurate and effective outreach.

Why Choose Success.ai’s HR Professionals Data?

1. Comprehensive Contact Information:
2. Access verified work emails, direct phone numbers, and LinkedIn profiles for HR leaders worldwide.

3. Data accuracy is backed by AI validation to ensure 99% reliability.

4. Global Reach Across HR Functions:

5. Includes profiles of HR directors, recruiters, payroll specialists, and training managers.

6. Covers regions such as North America, Europe, Asia-Pacific, South America, and the Middle East.

7. Continuously Updated Datasets:

8. Real-time updates provide the latest information about HR professionals in decision-making roles.

9. Ethical and Compliant:

10. Adheres to GDPR, CCPA, and other global privacy regulations for ethical use of data.

Data Highlights: - 170M+ Verified Professional Profiles: Includes HR professionals from diverse industries. - 50M Work Emails: Verified and AI-validated for seamless communication. - 30M Company Profiles: Rich insights to support detailed targeting. - 700M Global Professional Profiles: Enriched data for broad business objectives.

Key Features of the Dataset:

• HR Decision-Maker Profiles: Identify and connect with HR professionals at all levels, including C-suite HR leaders.
• Advanced Filters for Precision Targeting: Filter by industry, company size, location, and specific HR roles for precise results.
• AI-Driven Enrichment: Profiles enriched with actionable data for personalized engagement.

Strategic Use Cases:

1. Recruitment Solutions and HR Services:
2. Offer your HR technology, software, or services directly to decision-makers.

3. Build relationships with professionals managing recruitment, payroll, or employee engagement.

4. Corporate Training and Development:

5. Reach training managers to promote learning solutions, workshops, and skill-building programs.

6. Showcase personalized employee development initiatives.

7. Targeted Marketing Campaigns:

8. Design campaigns to promote HR-focused tools, resources, or consultancy services.

9. Leverage verified contact data for higher engagement and conversions.

10. HR Tech Solutions:

11. Present HR software, automation tools, or cloud solutions to relevant decision-makers.

12. Target professionals managing HR digital transformation.

Why Choose Success.ai?

1. Best Price Guarantee: Enjoy premium-quality datasets at competitive pricing.
2. Seamless Integration: Integrate data into your CRM using APIs or download datasets in preferred formats.
3. Data Accuracy with AI Validation: Confidence in 99% accuracy for all profiles included in the dataset.
4. Customizable and Scalable Solutions: Tailor data to your specific industry or HR role requirements.

APIs for Enhanced Functionality

1. Data Enrichment API: Enrich existing records with verified HR contact data.
2. Lead Generation API: Automate lead generation for HR-specific campaigns and initiatives.

Leverage B2B Contact Data for Human Resources Professionals Worldwide to connect with HR leaders and decision-makers in your target market. Success.ai offers verified work emails, phone numbers, and continuously updated profiles to ensure effective outreach and impactful communication.

With AI-validated accuracy and a Best Price Guarantee, Success.ai provides the ultimate solution for accessing and engaging global HR professionals. Contact us now to elevate your business strategy with precise and reliable data!

No one beats us on price. Period.

This dataset offers a comprehensive historical record of Netflix’s stock price movements, capturing the company’s financial journey from its early days to its position as a global streaming giant.

From its IPO in May 2002, Netflix (Ticker: NFLX) has transformed from a DVD rental service to a powerhouse in on-demand digital content. With its disruptive innovation, strategic shifts, and global expansion, Netflix has seen dramatic shifts in stock prices, reflecting not just market trends but also cultural impact. This dataset provides a window into that evolution.

What’s Included?

Each row in this dataset represents daily trading activity on the stock market and includes the following columns:

• Date – The trading day (from 2002 onward)
• Open – Stock price when the market opened
• High – Highest trading price of the day
• Low – Lowest trading price of the day
• Close – Final price at market close
• Adj Close – Closing price adjusted for splits and dividends
• Volume – Number of shares traded that day

The data is structured in CSV format and is clean, easy to use, and ready for immediate analysis.

Why Use This Dataset?

Whether you're learning data science, building a financial model, or exploring machine learning in the real world, this dataset is a goldmine of insights. Netflix's market history includes:

• Periods of explosive growth during digital transformation
• Volatility during market crashes and global events (e.g., 2008, COVID-19)
• Strategic pivots such as the shift to original content
• Market reactions to earnings, acquisitions, and subscriber milestones

This makes the dataset ideal for:

• Time-series forecasting (ARIMA, Prophet, LSTM)
• Technical and trend analysis (moving averages, RSI, Bollinger Bands)
• Predictive modeling with machine learning
• Investment simulation projects
• Stock market visualization and storytelling
• Financial dashboards (Tableau, Power BI, Streamlit, etc.)

Who Can Use It?

This dataset is designed for:

• Aspiring data scientists practicing EDA and modeling
• Financial analysts and traders exploring trends
• AI researchers working on time-series models
• Students building ML projects
• Developers creating stock visualization tools
• Kaggle competitors seeking real-world datasets

Data Source & Credits

The dataset is derived from publicly available historical stock price data, such as Yahoo Finance, and has been cleaned and organized for educational and research purposes. It is continuously maintained to ensure accuracy.

Start Exploring

Netflix’s rise is more than just a business story — it’s a data-driven journey. With this dataset, you can analyze the company’s stock behavior, train models to predict future trends, or simply visualize how tech reshapes the market.

📊 Job Postings Data for Talent Acquisition, HR Strategy & Market Research Canaria’s Job Postings Data product is a structured, AI-enriched dataset that captures and organizes millions of job listings from leading sources such as Indeed, LinkedIn, and other recruiting platforms. Designed for decision-makers in HR, strategy, and research, this data reveals workforce demand trends, employer activity, and hiring signals across the U.S. labor market and enhanced with advanced enrichment models.

The dataset enables clients to track who is hiring, what roles are being posted, which skills are in demand, where talent is needed geographically, and how compensation and employment structures evolve over time. With field-level normalization and deep enrichment, it transforms noisy job listings into high-resolution labor intelligence—optimized for strategic planning, analytics, and recruiting effectiveness.

🧠 Use Cases: What This Job Postings Data Solves This enriched dataset empowers users to analyze workforce activity, employer behavior, and hiring trends across sectors, geographies, and job categories.

🔍 Talent Acquisition & HR Strategy • Identify hiring trends by industry, company, function, and geography • Optimize job listings and outreach with enriched skill, title, and seniority data • Detect companies expanding or shifting their workforce focus • Monitor new roles and emerging skills in real time

📈 Labor Market Research & Workforce Planning • Visualize job market activity across cities, states, and ZIP codes • Analyze hiring velocity and job volume changes as macroeconomic signals • Correlate job demand with company size, sector, or compensation structure • Study occupational dynamics using AI-normalized job titles • Use directional signals (job increases/declines) to anticipate market shifts

📊 HR Analytics & Compensation Intelligence • Map salary ranges and benefits offerings by role, location, and level • Track high-demand or hard-to-fill positions for strategic workforce planning • Support compensation planning and headcount forecasting • Feed job title normalization and metadata into internal HRIS systems • Identify talent clusters and location-based hiring inefficiencies

🌐 What Makes This Job Postings Data Unique

🧠 AI-Based Enrichment at Scale • Extracted attributes include hard skills, soft skills, certifications, and education requirements • Modeled predictions for seniority level, employment type, and remote/on-site classification • Normalized job titles using an internal taxonomy of over 50,000 unique roles • Field-level tagging ensures structured, filterable, and clean outputs

💰 Salary Parsing & Compensation Insights • Parsed salary ranges directly from job descriptions • AI-based salary predictions for postings without explicit compensation • Compensation patterns available by job title, company, and location

🔁 Deduplication & Normalization • Achieves approximately 60% deduplication rate through semantic and metadata matching • Normalizes company names, job titles, location formats, and employment attributes • Ready-to-use, analysis-grade dataset—fully structured and cleansed

🔗 Company Matching & Metadata • Each job post is linked to a structured company profile, including metadata • Records are cross-referenced with LinkedIn and Google Maps to validate company identity and geography • Enables aggregation at employer or location level for deeper insights

🕒 Freshness & Scalability • Updated hourly to reflect real-time hiring behavior and job market shifts • Delivered in flexible formats (CSV, JSON, or data feed) and customizable filters • Supports segmentation by geography, company, seniority, salary, title, and more

🎯 Who Uses Canaria’s Job Postings Data • HR & Talent Teams – to benchmark roles, optimize pipelines, and compete for talent • Consultants & Strategy Teams – to guide clients with labor-driven insights • Market Researchers – to understand employment dynamics and job creation trends • HR Tech & SaaS Platforms – to power salary tools, job market dashboards, or recruiting features • Economic Analysts & Think Tanks – to model labor activity and hiring-based economic trends • BI & Analytics Teams – to build dashboards that track demand, skill shifts, and geographic patterns

📌 Summary Canaria’s Job Postings Data provides an AI-enriched, clean, and analysis-ready view of the U.S. job market. Covering millions of listings from Indeed, LinkedIn, other job boards, and ATS sources, it includes detailed job attributes, inferred compensation, normalized titles, skill extraction, and employer metadata—all updated hourly and fully structured.

With deep enrichment, reliable deduplication, and company matchability, this dataset is purpose-built for users needing workforce insights, market trends, and strategic talent intelligence. Whether you're modeling skill gaps, benchmarking compensation, or visualizing hiring momentum, this dataset provides a complete toolkit for HR and labor intellig...

📈 Microsoft Stock Price History (1986–2025)

This dataset provides daily historical stock price data for Microsoft Corporation (MSFT) from March 13, 1986 to April 6, 2025. It includes essential trading information such as open, high, low, close, adjusted close prices, and daily trading volume.

Whether you're a data scientist, financial analyst, or machine learning enthusiast, this dataset is perfect for building models, visualizing trends, or exploring the evolution of one of the world’s largest tech companies.

📂 Dataset Overview

📊 Summary

• Date Range: 1986-03-13 to 2025-04-06
• Total Entries: 9,843
• Average Close Price: ~$64.63
• Max Price (Close): $467.56
• Max Volume: Over 1 billion shares
• Missing Values: None ✅

🔍 Potential Use Cases

• Time-series forecasting using LSTM, ARIMA, or Prophet
• Backtesting trading strategies
• Analyzing long-term financial trends and volatility
• Visualizing market behavior around major events (e.g., dot-com bubble, COVID-19)
• Comparing real vs adjusted stock prices

💡 Project Ideas

• 📉 Predict next-day prices using deep learning
• 📈 Create interactive visualizations with Plotly
• 🧠 Train an ML model to detect bullish/bearish patterns
• 📊 Calculate technical indicators like RSI, MACD, Bollinger Bands

📎 License

This data is publicly available and intended for educational and research purposes only. For actual trading, always refer to a licensed financial data provider.

📬 Stay Connected

If you use this dataset in your project or research, feel free to share your work — I’d love to see it!

1-Kaggle: https://www.kaggle.com/muhammadatiflatif

2-Github: https://github.com/M-Atif-Latif

3-Linkdin: https://www.linkedin.com/in/muhammad-atif-latif-13a171318?utm_source=share&utm_campaign=share_via&utm_content=profile&utm_medium=android_app

4:X:

https://x.com/mianatif5867?s=09

🔗 LinkedIn Job Postings Data - Comprehensive Professional Intelligence for HR Strategy & Market Research

LinkedIn Job Postings Data represents the most comprehensive professional intelligence dataset available, delivering structured insights across millions of LinkedIn job postings, LinkedIn job listings, and LinkedIn career opportunities. Canaria's enriched LinkedIn Job Postings Data transforms raw LinkedIn job market information into actionable business intelligence—normalized, deduplicated, and enhanced with AI-powered enrichment for deep workforce analytics, talent acquisition, and market research.

This premium LinkedIn job postings dataset is engineered to help HR professionals, recruiters, analysts, and business strategists answer mission-critical questions: • What LinkedIn job opportunities are available in target companies? • Which skills are trending in LinkedIn job postings across specific industries? • How are companies advertising their LinkedIn career opportunities? • What are the salary expectations across different LinkedIn job listings and regions?

With real-time updates and comprehensive LinkedIn job posting enrichment, our data provides unparalleled visibility into LinkedIn job market trends, hiring patterns, and workforce dynamics.

🧠 Use Cases: What This LinkedIn Job Postings Data Solves

Our dataset transforms LinkedIn job advertisements, market information, and career listings into structured, analyzable insights—powering everything from talent acquisition to competitive intelligence and job market research.

Talent Acquisition & LinkedIn Recruiting Intelligence • LinkedIn job market mapping • LinkedIn career opportunity intelligence • LinkedIn job posting competitive analysis • LinkedIn job skills gap identification

HR Strategy & Workforce Analytics • Organizational network analysis • Employee mobility tracking • Compensation benchmarking • Diversity & inclusion analytics • Workforce planning intelligence • Skills evolution monitoring

Market Research & Competitive Intelligence • Company growth analysis • Industry trend identification • Competitive talent mapping • Market entry intelligence • Partnership & business development • Investment due diligence

LinkedIn Job Market Research & Economic Analysis • Regional LinkedIn job analysis • LinkedIn job skills demand forecasting • LinkedIn job economic impact assessment • LinkedIn job education-industry alignment • LinkedIn remote job trend analysis • LinkedIn career development ROI

🌐 What Makes This LinkedIn Job Postings Data Unique

AI-Enhanced LinkedIn Job Intelligence • LinkedIn job posting enrichment with advanced NLP • LinkedIn job seniority classification • LinkedIn job industry expertise mapping • LinkedIn job career progression modeling

Comprehensive LinkedIn Job Market Intelligence • Real-time LinkedIn job postings with salary, requirements, and company insights • LinkedIn recruiting activity tracking • LinkedIn job application analytics • LinkedIn job skills demand analysis • LinkedIn compensation intelligence

Company & Organizational Intelligence • Company growth indicators • Cultural & values intelligence • Competitive positioning

LinkedIn Job Data Quality & Normalization • Advanced LinkedIn job deduplication • LinkedIn job skills taxonomy standardization • LinkedIn job geographic normalization • LinkedIn job company matching • LinkedIn job education standardization

🎯 Who Uses Canaria's LinkedIn Data

HR & Talent Acquisition Teams • Optimize recruiting pipelines • Benchmark compensation • Identify talent pools • Develop data-driven hiring strategies

Market Research & Intelligence Analysts • Track industry trends • Build competitive intelligence models • Analyze workforce dynamics

HR Technology & Analytics Platforms • Power recruiting tools and analytics solutions • Fuel compensation engines and dashboards

Academic & Economic Researchers • Study labor market dynamics • Analyze career mobility trends • Research professional development

Government & Policy Organizations • Evaluate workforce development programs • Monitor skills gaps • Inform economic initiatives

📌 Summary

Canaria's LinkedIn Job Postings Data delivers the most comprehensive LinkedIn job market intelligence available. It combines job posting insights, recruiting intelligence, and organizational data in one unified dataset. With AI-enhanced enrichment, real-time updates, and enterprise-grade data quality, it supports advanced HR analytics, talent acquisition, job market research, and competitive intelligence.

🏢 About Canaria Inc. Canaria Inc. is a leader in alternative data, specializing in job market intelligence, LinkedIn company data, Glassdoor salary analytics, and Google Maps location insights. We deliver clean, structured, and enriched datasets at scale using proprietary data scraping pipelines and advanced AI/LLM-based modeling, all backed by human validation. Our platform also includes Google Maps data, providing verified business locatio...

Tweet Sentiment's Impact on Stock Returns

862,231 Labeled Instances

By [source]

About this dataset

This dataset contains 862,231 labeled tweets and associated stock returns, providing a comprehensive look into the impact of social media on company-level stock market performance. For each tweet, researchers have extracted data such as the date of the tweet and its associated stock symbol, along with metrics such as last price and various returns (1-day return, 2-day return, 3-day return, 7-day return). Also recorded are volatility scores for both 10 day intervals and 30 day intervals. Finally, sentiment scores from both Long Short - Term Memory (LSTM) and TextBlob models have been included to quantify the overall tone in which these messages were delivered. With this dataset you will be able to explore how tweets can affect a company's share prices both short term and long term by leveraging all of these data points for analysis!

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How to use the dataset

In order to use this dataset, users can utilize descriptive statistics such as histograms or regression techniques to establish relationships between tweet content & sentiment with corresponding stock return data points such as 1-day & 7-day returns measurements.

The primary fields used for analysis include Tweet Text (TWEET), Stock symbol (STOCK), Date (DATE), Closing Price at the time of Tweet (LAST_PRICE) a range of Volatility measures 10 day Volatility(VOLATILITY_10D)and 30 day Volatility(VOLATILITY_30D ) for each Stock which capture changes in market fluctuation during different periods around when Twitter reactions occur. Additionally Sentiment Polarity analysis undertaken via two Machine learning algorithms LSTM Polarity(LSTM_POLARITY)and Textblob polarity provide insight into whether people are expressing positive or negative sentiments about each company at given times which again could influence thereby potentially influence Stock Prices over shorter term periods like 1-Day Returns(1_DAY_RETURN),2-Day Returns(2_DAY_RETURN)or longer term horizon like 7 Day Returns*7DAY RETURNS*.Finally MENTION field indicates if names/acronyms associated with Companies were specifically mentioned in each Tweet or not which gives extra insight into whether company specific contexts were present within individual Tweets aka “Company Relevancy”

Research Ideas

• Analyzing the degree to which tweets can influence stock prices. By analyzing relationships between variables such as tweet sentiment and stock returns, correlations can be identified that could be used to inform investment decisions.
• Exploring natural language processing (NLP) models for predicting future market trends based on textual data such as tweets. Through testing and evaluating different text-based models using this dataset, better predictive models may emerge that can give investors advance warning of upcoming market shifts due to news or other events.
• Investigating the impact of different types of tweets (positive/negative, factual/opinionated) on stock prices over specific time frames. By studying correlations between the sentiment or nature of a tweet and its effect on stocks, insights may be gained into what sort of news or events have a greater impact on markets in general

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: reduced_dataset-release.csv | Column name | Description | |:----------------------|:-------------------------------------------------------------------------------------------------------| | TWEET | Text of the tweet. (String) | | STOCK | Company's stock mentioned in the tweet. (String) | | DATE | Date the tweet was posted. (Date) | | LAST_PRICE | Company's last price at the time of tweeting. (Float) ...

📊 Google Data for Market Intelligence, Business Validation & Lead Enrichment Google Data is one of the most valuable sources of location-based business intelligence available today. At Canaria, we’ve built a robust, scalable system for extracting, enriching, and delivering verified business data from Google Maps—turning raw location profiles into high-resolution, actionable insights.

Our Google Maps Company Profile Data includes structured metadata on businesses across the U.S., such as company names, standardized addresses, geographic coordinates, phone numbers, websites, business categories, open hours, diversity and ownership tags, star ratings, and detailed review distributions. Whether you're modeling a market, identifying leads, enriching a CRM, or evaluating risk, our Google Data gives your team an accurate, up-to-date view of business activity at the local level.

This dataset is updated weekly, and is fully customizable—allowing you to pull exactly what you need, whether you're targeting a specific geography, industry segment, review range, or open-hour window.

🌎 What Makes Canaria’s Google Data Unique? • Location Precision – Every business record is enriched with latitude/longitude, ZIP code, and Google Plus Code to ensure exact geolocation • Reputation Signals – Review tags, star ratings, and review counts are included to allow brand sentiment scoring and risk monitoring • Diversity & Ownership Tags – Capture public-facing declarations such as “women-owned” or “Asian-owned” for DEI, ESG, and compliance applications • Contact Readiness – Clean, standardized phone numbers and domains help teams route leads to sales, support, or customer success • Operational Visibility – Up-to-date open hours, categories, and branch information help validate which locations are active and when

Our data is built to be matched, integrated, and analyzed—and is trusted by clients in financial services, go-to-market strategy, HR tech, and analytics platforms.

🧠 What This Google Data Solves Canaria Google Data answers critical operational, market, and GTM questions like:

• Which businesses are actively operating in my target region or category? • Which leads are real, verified, and tied to an actual physical branch? • How can I detect underperforming companies based on review sentiment? • Where should I expand, prospect, or invest based on geographic presence? • How can I enhance my CRM, enrichment model, or targeting strategy using location-based data?

✅ Key Use Cases for Google Maps Business Data Our clients leverage Google Data across a wide spectrum of industries and functions. Here are the top use cases:

🔍 Lead Scoring & Business Validation • Confirm the legitimacy and physical presence of potential customers, partners, or competitors using verified Google Data • Rank leads based on proximity, star ratings, review volume, or completeness of listing • Filter spammy or low-quality leads using negative review keywords and tag summaries • Validate ABM targets before outreach using enriched business details like phone, website, and hours

📍 Location Intelligence & Market Mapping • Visualize company distributions across geographies using Google Maps coordinates and ZIPs • Understand market saturation, density, and white space across business categories • Identify underserved ZIP codes or local business deserts • Track presence and expansion across regional clusters and industry corridors

⚠️ Company Risk & Brand Reputation Scoring • Monitor Google Maps reviews for sentiment signals such as “scam”, “spam”, “calls”, or service complaints • Detect risk-prone or underperforming locations using star rating distributions and review counts • Evaluate consistency of open hours, contact numbers, and categories for signs of listing accuracy or abandonment • Integrate risk flags into investment models, KYC/KYB platforms, or internal alerting systems

🗃️ CRM & RevOps Enrichment • Enrich CRM or lead databases with phone numbers, web domains, physical addresses, and geolocation from Google Data • Use business category classification for segmentation and routing • Detect duplicates or outdated data by matching your records with the most current Google listing • Enable advanced workflows like field-based rep routing, localized campaign assignment, or automated ABM triggers

📈 Business Intelligence & Strategic Planning • Build dashboards powered by Google Maps data, including business counts, category distributions, and review activity • Overlay business presence with population, workforce, or customer base for location planning • Benchmark performance across cities, regions, or market verticals • Track mobility and change by comparing past and current Google Maps metadata

💼 DEI, ESG & Ownership Profiling • Identify minority-owned, women-owned, or other diversity-flagged companies using Google Data ownership attributes • Build datasets aligned with supplier diversity mandates or ESG investment strategies • Segment location insi...

Techsalerator’s Sound and Audio Data for Benin

Techsalerator’s Sound and Audio Data for Benin provides a crucial and extensive collection of information for businesses, researchers, and industry professionals. This dataset offers an in-depth analysis of sound and audio-related activities, including market trends, technological advancements, and audio content distribution.

For access to the full dataset, contact us at info@techsalerator.com or visit Techsalerator Contact Us.

Techsalerator’s Sound and Audio Data for Benin

Techsalerator’s Sound and Audio Data for Benin delivers a comprehensive overview of the country's audio landscape. This dataset includes insights into sound recording, broadcasting, music streaming, podcasting, and acoustic research, offering valuable data for businesses, media professionals, and sound engineers.

Top 5 Key Data Fields

• Audio Content Producers: Identifies major players in the industry, including radio stations, podcast creators, and music production companies.
• Sound Recording and Studios: Lists recording studios, their capabilities, and services available for audio production and post-production.
• Streaming and Distribution Platforms: Provides insights into music and audio content distribution, including streaming services and digital platforms operating in Benin.
• Broadcasting and Radio Stations: Covers AM/FM and digital radio stations, their market reach, and programming.
• Audio Technology and Innovations: Details advancements in sound technology, including research on acoustic engineering and audio equipment developments.

Top 5 Sound and Audio Trends in Benin

• Rise of Digital Streaming: Increasing adoption of online streaming platforms for music, podcasts, and audiobooks.
• Growth in Podcasting: More local content creators producing podcasts covering various topics from culture to business and entertainment.
• Expansion of Community Radio: Community-based radio stations gaining popularity for delivering local news, entertainment, and cultural programming.
• Advancements in Audio Equipment: Growing demand for professional audio gear, including microphones, mixers, and editing software.
• Integration of AI in Sound Processing: AI-powered tools being used for audio editing, sound enhancement, and music recommendations.

Top 5 Companies Leading in Sound and Audio Data in Benin

• Benin Music Hub: A leading music production and distribution company supporting local artists and content creators.
• Radio Benin FM: A prominent broadcasting network providing news, entertainment, and cultural programs.
• PodCast Benin: A fast-growing podcasting platform featuring local and international content.
• SoundTech Solutions: An innovative company specializing in audio engineering, sound recording, and acoustic technology.
• Benin Audio Labs: A research-focused organization exploring advancements in sound quality, noise reduction, and AI-driven audio solutions.

Accessing Techsalerator’s Sound and Audio Data

To obtain Techsalerator’s Sound and Audio Data for Benin, contact info@techsalerator.com with your specific data requirements. Techsalerator provides a customized quote based on the requested data fields and records, with delivery available within 24 hours. Ongoing access options can also be arranged.

Included Data Fields

• Audio Content Producers
• Sound Recording and Studios
• Streaming and Distribution Platforms
• Broadcasting and Radio Stations
• Audio Technology and Innovations
• Music Production and Licensing
• Podcasting and New Media
• Acoustic Research and Sound Engineering
• Market Trends and Consumer Behavior
• Contact Information

For detailed insights into sound and audio activities in Benin, Techsalerator’s dataset is an essential resource for businesses, researchers, and industry professionals aiming to make informed and strategic decisions.

US Airlines Twitter (Over time)

Study the trend customer satisfaction over time

About this dataset

The columns in the dataset include index, unit id, golden, unit state, trusted judgments, last judgment at, airline sentiment, airline sentiment confidence, negative reason, negative reason confidence, airline_sentiment_gold and retweet count. There is also text included for each tweet as well as tweet location and user timezone.

Using this dataset, you can get a feel for how customers of various airlines feel about their service. You can use the data to analyze trends over time or compare different airlines. Some research ideas include using airline sentiment to predict the stock market or using the negativereason data to help airlines improve their customer service

How to use the dataset

Looking at this dataset, you can get a feel for how customers of various airlines feel about their service. The data includes the airline, the tweet text, the date of the tweet, and various other information. You can use this to analyze trends over time or compare different airlines

Research Ideas

• Using airline sentiment to predict the stock market - is there a correlation between how the public perceives an airline and how that airline's stock performs?
• Using negativereason data to help airlines improve their customer service - which negative reasons are mentioned most often? Are there certain airlines that are consistently mentioned for specific reasons?
• Use the tweet data to map out airline hot spots - where do people tend to tweet about certain airlines the most? Is there a geographic pattern to sentiment about specific airlines?

Acknowledgements

If you use this dataset in your research, please credit Social Media Data

License

License: Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) - You are free to: - Share - copy and redistribute the material in any medium or format for non-commercial purposes only. - Adapt - remix, transform, and build upon the material for non-commercial purposes only. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - You may not: - Use the material for commercial purposes.

Columns

File: Airline-Sentiment-2-w-AA.csv | Column name | Description | |:---------------------------|:-----------------------------------------------------------------------------| | _golden | This column is the gold standard column. (Boolean) | | _unit_state | This column is the state of the unit. (String) | | _trusted_judgments | This column is the number of trusted judgments. (Numeric) | | _last_judgment_at | This column is the timestamp of the last judgment. (String) | | airline_sentiment | This column is the sentiment of the tweet. (String) | | negativereason | This column is the negative reason for the sentiment. (String) | | airline_sentiment_gold | This column is the gold standard sentiment of the tweet. (String) | | name | This column is the name of the airline. (String) | | negativereason_gold | This column is the gold standard negative reason for the sentiment. (String) | | retweet_count | This column is the number of retweets. (Numeric) | | text | This column is the text of the tweet. (String) | | tweet_coord | This column is the coordinates of the tweet. (String) | | tweet_created | This column is the timestamp of the tweet. (String) | | tweet_location | This column is the location of the tweet. (String) | | user_timezone | This column is the timezone of the user. (String) |

Forager.ai | 733M+ Global Job Postings Dataset – Hiring Insights and Workforce Trends.

Gain a competitive edge with Forager.ai’s Global Job Postings Dataset, featuring 733M+ records of real-time job postings across industries, locations, and companies. With 97M+ updates monthly, our data is updated hourly, ensuring you always have the most accurate and timely insights available. Powered by AI-driven data curation, our dataset provides job titles, company details, salary data, requirements, and more.

Key Features & Stats:

733M+ Total Records: The most comprehensive job postings dataset available.

97M+ Monthly Updates: Stay ahead with frequent, industry-leading refresh rates.

AI-Powered Curation: Accurate, high-quality data tailored to your needs.

Flexible Formats: Available in JSON and CSV for easy API integration.

Key Datapoints:

Job Titles, Company Info, Job description, Remote or on site vacancies, Employee Benefits, and more.

Normalized Job Titles for consistent classification.

Linkable to Company Data: Combine with Forager’s Company Dataset for deeper insights.

Use Cases:

Sales Platforms & ABM: Real-time insights into companies hiring now.

Recruitment Solutions: Enhance job matching and outreach with fresh data.

Venture Capital & PE: Track emerging startups and their hiring growth.

HR Tech & ATS Platforms: Empower your clients with up-to-date job trends.

Why Choose Forager.ai?

Unmatched Coverage & Refresh Rate: Over 733M records with 97M+ updates/month.

Real-Time Insights: Data updated every hour to keep you informed.

Comprehensive & Accurate: AI-curated to ensure relevance and high quality.

Flexible Delivery: Choose from S3, PostgreSQL, or REST API options.

Explore Forager.ai’s Job Postings Dataset and gain actionable insights to drive your recruitment, sales, and market research strategies. Contact us today for more details!

➡️ You can choose from multiple data formats, delivery frequency options, and delivery methods

➡️ Extensive web datasets with job posting data from 5 leading B2B data sources

➡️ Jobs API designed for effortless search and enrichment (accessible using a user-friendly self-service tool)

➡️ Fresh data: daily updates, easy change tracking with dedicated data fields, and a constant flow of new data

➡️ You get all the necessary resources for evaluating our web dataset: a free consultation, a data sample, or free credits for testing the API.

✅ For HR tech

Job posting web dataset can provide insights into the demand for different types of jobs and skills, as well as trends in job postings over time. With access to historical data, companies can develop predictive models.

✅ For Investors

Explore expansion trends, analyze hiring practices, and predict company or industry growth rates, enabling the extraction of actionable strategic and operational insights. At a larger scale of analysis, Job Postings Database can be leveraged to forecast market trends and predict the growth of specific industries.

✅ For Lead generation

Coresignal’s Job Postings Data is ideal for lead generation and determining purchasing intent. In B2B sales, job postings can help identify the best time to approach a prospective client.

➡️ Why 400+ data-powered businesses choose Coresignal:

1. Experienced data provider (in the market since 2016)
2. Exceptional client service
3. Responsible and secure data collection

Forager.ai's Small Business Contact Data set is a comprehensive collection of over 695M professional profiles. With an unmatched 2x/month refresh rate, we ensure the most current and dynamic data in the industry today. We deliver this data via JSONL flat-files or PostgreSQL database delivery, capturing publicly available information on each profile.

| Volume and Stats |

Every single record refreshed 2x per month, setting industry standards. First-party data curation powering some of the most renowned sales and recruitment platforms. Delivery frequency is hourly (fastest in the industry today). Additional datapoints and linkages available. Delivery formats: JSONL, PostgreSQL, CSV. | Datapoints |

Over 150+ unique datapoints available! Key fields like Current Title, Current Company, Work History, Educational Background, Location, Address, and more. Unique linkage data to other social networks or contact data available. | Use Cases |

Sales Platforms, ABM Vendors, Intent Data Companies, AdTech and more:

Deliver the best end-customer experience with our people feed powering your solution! Be the first to know when someone changes jobs and share that with end-customers. Industry-leading data accuracy. Connect our professional records to your existing database, find new connections to other social networks, and contact data. Hashed records also available for advertising use-cases. Venture Capital and Private Equity:

Track every company and employee with a publicly available profile. Keep track of your portfolio's founders, employees and ex-employees, and be the first to know when they move or start up. Keep an eye on the pulse by following the most influential people in the industries and segments you care about. Provide your portfolio companies with the best data for recruitment and talent sourcing. Review departmental headcount growth of private companies and benchmark their strength against competitors. HR Tech, ATS Platforms, Recruitment Solutions, as well as Executive Search Agencies:

Build products for industry-specific and industry-agnostic candidate recruiting platforms. Track person job changes and immediately refresh profiles to avoid stale data. Identify ideal candidates through work experience and education history. Keep ATS systems and candidate profiles constantly updated. Link data from this dataset into GitHub, LinkedIn, and other social networks. | Delivery Options |

Flat files via S3 or GCP PostgreSQL Shared Database PostgreSQL Managed Database REST API Other options available at request, depending on scale required | Other key features |

Over 120M US Professional Profiles. 150+ Data Fields (available upon request) Free data samples, and evaluation. Tags: Professionals Data, People Data, Work Experience History, Education Data, Employee Data, Workforce Intelligence, Identity Resolution, Talent, Candidate Database, Sales Database, Contact Data, Account Based Marketing, Intent Data.

🔗 Skill Taxonomy Data US | AI-Powered Title & Skill Taxonomy Matched with Job Postings for Talent Intelligence & Workforce Planning

Our Skill Taxonomy Data product offers a comprehensive, AI-driven hierarchical mapping of job titles and associated skills, certifications, and qualifications — precisely matched to millions of job postings across the US labor market. This data empowers HR professionals, talent managers, and workforce planners with unparalleled insights for strategic decision-making.

Harnessing state-of-the-art AI and large language models (LLMs) validated by human experts, our skill taxonomy data surpasses traditional keyword matching by leveraging advanced entity recognition, contextual filtering, and skill relevance ranking. This ensures highly accurate, actionable intelligence for talent acquisition, HR analytics, and workforce development.

🧠 What the Skill Taxonomy Data Solves Our data transforms unstructured job market information into a dynamic, normalized taxonomy of skills and titles — addressing key questions such as:

• What specific hard and soft skills are demanded across job titles and industries? • How do certifications and qualifications align with workforce requirements? • Which skills are emerging or declining in demand for targeted workforce planning? • How can organizations benchmark and close skill gaps effectively? • What hierarchical skill relationships support career development and succession planning?

⚙️ Key Features & Capabilities ✅ Normalized Titles & Skills • ~70,000 unique normalized job titles (e.g., Human Resources Generalist) • Up to 20 hard skills per title (~37,000 unique skills, e.g., Python) • Up to 10 soft skills per title (~400 unique, e.g., Communication) • ~3,000 unique certifications (e.g., PMP) and ~8 standardized qualification levels (e.g., Bachelor’s) • Weighted relevance scores based on frequency, uniqueness, and contextual significance

🧠 Advanced AI & NLP Enrichment • Contextual entity recognition using NER, embeddings, MinHash, and AI-based filtering • Disambiguation of skill variants (e.g., ML → Machine Learning) • Exclusion of irrelevant entities based on job role context

🗂 Hierarchical Skill Mapping & Descriptions • Structured taxonomy from broad categories to granular capabilities • Clear skill definitions for use in HR, L&D, and employee development

📊 Demand & Supply Analytics • Real-time and historical tracking of skill demand from 600M+ job postings • Visibility into skill clusters, their market value, and projected workforce needs

🔎 Interactive Search & Insights • Searchable skill-to-title mapping for recruitment and internal mobility • Support for targeted training programs and career pathing initiatives

🔁 Continuous Updates & Market Responsiveness • Monthly data refreshes reflecting changes in the labor market and evolving tech landscape

🧩 Comprehensive Workforce Management Support • Actionable insights to optimize hiring, reskill existing employees, and plan succession effectively

📈 Scalable & Customizable Solutions • Adaptable across industries and organization sizes • Customizable to support bespoke strategic HR and analytics initiatives

🧠 Use Cases & Benefits • Human Resources (HR): Streamline recruitment by identifying critical role-specific skills and certifications • Talent Management: Design L&D programs aligned with in-demand and emerging skills • HR Consulting: Deliver evidence-based talent diagnostics and strategic workforce solutions • Human Resources Planning: Forecast and prepare for evolving organizational skill needs • HR Analytics: Detect and analyze skill trends for better workforce and talent decisions

💡 What Makes This Skill Taxonomy Data Unique • AI-Validated Skill Recognition: Goes beyond keyword matching using contextual LLMs and AI models • Data Depth & Breadth: Covers over 600M US job postings across industries, refreshed monthly • Weighted Skill Importance: Context-aware scoring system suppresses generic skills and highlights role-specific needs • Rich Metadata: Includes weights, skill definitions, certifications, and qualifications — integrated into a structured hierarchy • Seamless Integration: Easily embedded into HRIS, ATS, L&D, and workforce analytics tools

👥 Who Uses Our Skill Taxonomy Data • HR & Talent Acquisition Teams – For sourcing, screening, and candidate-job matching • Learning & Development Managers – For designing targeted training and upskilling programs • Workforce Planning & Analytics Teams – To anticipate future hiring and skill needs • HR Consultants & Analysts – For delivering actionable talent strategy and diagnostics • Business Leaders & Strategy Teams – To inform competitive workforce and organizational strategies

🏢 About Canaria Inc. Canaria Inc. is a leader in alternative data, specializing in job market intelligence, LinkedIn company data, and Glassdoor salary analytics. We deliver clean, structured, and enriched datasets at scale using proprietary data scraping pipe...