Introduction

The Job Market Insights Dataset offers a comprehensive view of job postings worldwide, providing critical data on job roles, salaries, qualifications, locations, and company profiles. This dataset serves as a valuable resource for understanding global employment trends and patterns in various industries.

Objective

The primary objective of analyzing this dataset is to gain actionable insights into job market dynamics, including in-demand skills, salary ranges by role, preferred qualifications, and geographical job distributions. This analysis can empower job seekers, recruiters, and businesses to make informed decisions.

Key Features

1. Diverse Job Roles: Includes details for various professions like Network Engineers, Software Testers, UX/UI Designers, and more.
2. Global Scope: Covers jobs from diverse locations, spanning countries and industries worldwide.
3. Comprehensive Data Points: Provides salary ranges, qualifications, job types, company profiles, and benefits offered.
4. Temporal Data: Captures job posting dates to understand trends over time.
5. Skills and Responsibilities: Details required skills and responsibilities, aiding in understanding role-specific requirements.

Benefits for Data Science

• Predictive Modeling: Build models to predict salaries, skill demands, or the probability of job fulfillment.
• Trend Analysis: Identify trends in job roles, qualifications, and compensation.
• Geospatial Analysis: Map job distributions to uncover opportunities in specific regions.
• Clustering & Segmentation: Segment jobs by industry, role, or qualifications for targeted insights.
• Skill Gap Identification: Analyze skill requirements to identify gaps between current offerings and market demands.

This dataset is a goldmine for extracting insights that can optimize recruitment strategies, guide career planning, and inform educational initiatives.

This dataset contains information on various products sold on Amazon, including their name, product dimensions, and a description. The dataset may contain missing values and messy data, requiring the analyst to perform various NLP tasks such as data cleaning and feature extraction. The bullet points column contains some of the main features of each product, which may be useful for analysis. Additionally, the products are classified into different types based on various parameters. The dataset provides an opportunity for data scientists to practice their NLP skills and extract insights from a large and diverse set of Amazon products.

The size of the Goat Rue Extract market was valued at USD XXX million in 2024 and is projected to reach USD XXX million by 2033, with an expected CAGR of XX% during the forecast period.

Market Research Intellect's Data Extraction Tools Market Report highlights a valuation of USD 2.5 billion in 2024 and anticipates growth to USD 5.1 billion by 2033, with a CAGR of 8.9% from 2026-2033.Explore insights on demand dynamics, innovation pipelines, and competitive landscapes.

Dataset

This dataset was created by Abdelaziz Sami

Released under Apache 2.0

Contents

Market Research Intellect's Extract Betaine Market Report highlights a valuation of USD 1.5 billion in 2024 and anticipates growth to USD 2.5 billion by 2033, with a CAGR of 7.2% from 2026-2033.Explore insights on demand dynamics, innovation pipelines, and competitive landscapes.

The report offers Plant DNA Extraction Kit Market Dynamics, Comprises Industry development drivers, challenges, opportunities, threats and limitations. A report also incorporates Cost Trend of products, Mergers & Acquisitions, Expansion, Crucial Suppliers of products, Concentration Rate of Steel Coupling Economy. Global Plant DNA Extraction Kit Market Research Report covers Market Effect Factors investigation chiefly included Technology Progress, Consumer Requires Trend, External Environmental Change.

A crucial part of sentiment classification is featuring extraction because it involves extracting valuable information from text data, which affects the model’s performance. The goal of this paper is to help in selecting a suitable feature extraction method to enhance the performance of sentiment analysis tasks. In order to provide directions for future machine learning and feature extraction research, it is important to analyze and summarize feature extraction techniques methodically from a machine learning standpoint. There are several methods under consideration, including Bag-of-words (BOW), Word2Vector, N-gram, Term Frequency- Inverse Document Frequency (TF-IDF), Hashing Vectorizer (HV), and Global vector for word representation (GloVe). To prove the ability of each feature extractor, we applied it to the Twitter US airlines and Amazon musical instrument reviews datasets. Finally, we trained a random forest classifier using 70% of the training data and 30% of the testing data, enabling us to evaluate and compare the performance using different metrics. Based on our results, we find that the TD-IDF technique demonstrates superior performance, with an accuracy of 99% in the Amazon reviews dataset and 96% in the Twitter US airlines dataset. This study underscores the paramount significance of feature extraction in sentiment analysis, endowing pragmatic insights to elevate model performance and steer future research pursuits.

Chat Highlight Extraction Tools Market Outlook

As per our latest research, the global Chat Highlight Extraction Tools market size reached USD 1.32 billion in 2024, reflecting robust adoption across diverse sectors. The market is expected to grow at a CAGR of 15.7% from 2025 to 2033, projecting a value of USD 4.73 billion by 2033. This remarkable growth trajectory is primarily driven by the increasing demand for real-time data insights, automation, and enhanced customer engagement across enterprises worldwide.

The primary growth factor propelling the Chat Highlight Extraction Tools market is the exponential rise in digital communication channels. Organizations are increasingly leveraging chat platforms for internal collaboration, customer support, and external communications, leading to an overwhelming volume of unstructured conversational data. The need to extract actionable insights from these conversations has never been more critical. Chat highlight extraction tools, powered by advanced artificial intelligence and natural language processing, offer a solution by automatically identifying, summarizing, and categorizing key information from chat logs. This capability enables businesses to improve decision-making, enhance productivity, and deliver superior customer experiences, fueling market expansion.

Another significant driver is the surge in remote and hybrid work models, especially post-pandemic. As teams become more geographically dispersed, efficient collaboration tools are essential for maintaining productivity and ensuring seamless communication. Chat highlight extraction tools enable teams to quickly identify important discussion points, decisions, and follow-ups from chat transcripts, thus streamlining workflows and reducing the risk of information loss. The integration of these tools with popular communication platforms like Slack, Microsoft Teams, and WhatsApp further enhances their utility, contributing to widespread adoption across small, medium, and large enterprises.

Moreover, the growing emphasis on customer-centric strategies across industries is catalyzing the adoption of chat highlight extraction tools in customer support operations. By automating the extraction of critical customer queries, complaints, and feedback from chat interactions, organizations can respond more effectively and personalize their services. This not only improves customer satisfaction but also enables proactive issue resolution and trend analysis. The increasing use of chatbots and virtual assistants in customer service further amplifies the need for sophisticated extraction tools to monitor, analyze, and optimize conversational data at scale.

From a regional perspective, North America dominates the Chat Highlight Extraction Tools market, accounting for the largest revenue share in 2024, followed closely by Europe and Asia Pacific. The high adoption rate in North America can be attributed to the presence of leading technology firms, advanced IT infrastructure, and a strong focus on digital transformation. Europe is witnessing significant growth driven by stringent data privacy regulations and increasing investments in AI-driven solutions. Meanwhile, the Asia Pacific region is emerging as a lucrative market, propelled by rapid digitalization, expanding enterprise sector, and rising awareness about the benefits of conversational analytics.

Component Analysis

The Chat Highlight Extraction Tools market is segmented by component into software and services. The software segment holds the largest market share, primarily due to the rapid advancements in artificial intelligence, machine learning, and natural language processing technologies. These software solutions are designed to automatically extract, summarize, and categorize key highlights from chat conversations, offering seamless integration with various communication platforms. The increasing demand for scalable, user-friendly, and customizable software tools is further driving this segment’s growth, as organizations seek to automate an

Access expert Dna And Rna Extraction Kit Market research covering data intelligence and growth analysis. Syndicated reports for strategic decision-making and business planning.

Extracting Insights from Online DiscussionsReddit is one of the largest social discussion platforms, making it a valuable source for real-time opinions, trends, sentiment analysis, and user interactions across various industries. Scraping Reddit data allows businesses, researchers, and analysts to explore public discussions, track sentiment, and gain actionable insights from user-generated content. Benefits and Impact: Trend […]

The global Web Scraping Services market is poised for robust expansion, projected to reach a significant valuation of approximately $2.5 billion in 2025. This growth is underpinned by a Compound Annual Growth Rate (CAGR) of roughly 18%, indicating a dynamic and rapidly evolving industry. The surge in demand for real-time data across various sectors, including market research, customer insight analysis, and data aggregation, is a primary driver. Businesses are increasingly relying on web scraping to gather competitive intelligence, monitor market trends, and personalize customer experiences. The proliferation of e-commerce and the digital transformation initiatives across industries further fuel the need for efficient data extraction solutions. Moreover, the growing adoption of AI and machine learning for data analysis amplifies the value proposition of web scraping services, enabling businesses to derive deeper insights from scraped data. This sustained demand, coupled with technological advancements, positions the web scraping services market for considerable growth throughout the forecast period. The market is characterized by several key trends and segments. The "Browser Extension" segment is gaining traction due to its user-friendly interface and accessibility for individual users and small businesses. Simultaneously, "Installable Software" continues to be a strong contender for enterprises requiring more robust and customizable solutions. "Cloud-Based" services are also witnessing significant adoption, offering scalability and cost-effectiveness. Geographically, North America, particularly the United States, is expected to lead the market, driven by its advanced technological infrastructure and a high concentration of data-intensive industries. Asia Pacific, led by China and India, is anticipated to exhibit the fastest growth due to increasing digital penetration and a burgeoning startup ecosystem. While the market exhibits strong growth potential, potential restraints such as evolving legal and ethical considerations around data privacy and the increasing complexity of website anti-scraping measures will require continuous adaptation and innovation from service providers to maintain momentum. This report provides a comprehensive analysis of the global Web Scraping Services market, offering insights into market dynamics, key players, emerging trends, and future growth prospects. The market, valued at an estimated $2.1 billion in 2023, is projected to reach $4.8 billion by 2029, exhibiting a Compound Annual Growth Rate (CAGR) of 14.5%.

The global natural cotton extract market is gaining traction due to rising consumer preference for plant-based, chemical-free, and biodegradable ingredients across personal care, pharmaceuticals, and textile applications. The market is projected to grow from USD 15,778.2 million in 2025 to USD 22,936.4 million by 2035, reflecting a CAGR of 3.8% during the forecast period.

Metric	Value
Market Size (2025E)	USD 15,778.2 million
Market Value (2035F)	USD 22,936.4 million
CAGR (2025 to 2035)	3.8%

Country-Wise Outlook

Country	CAGR (2025 to 2035)
United States	3.6%

Country	CAGR (2025 to 2035)
United Kingdom	3.4%

Region	CAGR (2025 to 2035)
European Union	3.5%

Country	CAGR (2025 to 2035)
Japan	3.4%

Country	CAGR (2025 to 2035)
South Korea	3.6%

Segmentation Outlook

Product Type Segment	Market Share (2025)
Powder Extract	57.2%

Application Segment	Market Share (2025)
Personal Care Products	48.6%

Competitive Outlook

Company Name	Estimated Market Share (%)
Botanicals Plus	18-21%
Carrubba Inc.	14-17%
The Garden of Naturals	11-14%
KCC Beauty	9-12%
Others	35-40%

The text analytics market is experiencing robust growth, projected to reach $10.49 billion in 2025 and exhibiting a remarkable Compound Annual Growth Rate (CAGR) of 39.90% from 2019 to 2033. This expansion is fueled by several key drivers. The increasing volume of unstructured data generated across various industries, including healthcare, finance, and customer service, necessitates sophisticated tools for extracting actionable insights. Furthermore, advancements in natural language processing (NLP), machine learning (ML), and artificial intelligence (AI) are empowering text analytics solutions with enhanced capabilities, such as sentiment analysis, topic modeling, and entity recognition. The rising adoption of cloud-based solutions also contributes to market growth, offering scalability, cost-effectiveness, and ease of access. Major industry players like IBM, Microsoft, and SAP are actively investing in research and development, driving innovation and expanding the market's capabilities. Competitive pressures are fostering a continuous improvement in the accuracy and efficiency of text analytics tools, making them increasingly attractive to businesses of all sizes. The growing demand for real-time insights and improved customer experience further propels market expansion. While the market enjoys significant growth momentum, certain challenges persist. Data security and privacy concerns remain paramount, necessitating robust security measures within text analytics platforms. The complexity of implementing and integrating these solutions into existing IT infrastructures can also pose a barrier to adoption, particularly for smaller businesses lacking dedicated data science teams. Furthermore, the accuracy and reliability of text analytics outputs can be affected by the quality and consistency of the input data. Overcoming these challenges through improved data governance, user-friendly interfaces, and robust customer support will be crucial for continued market expansion. Despite these restraints, the overall market outlook remains positive, driven by the continuous evolution of technology and the growing reliance on data-driven decision-making across diverse sectors. Recent developments include: January 2023- Microsoft announced a new multibillion-dollar investment in ChatGPT maker Open AI. ChatGPT, automatically generates text based on written prompts in a more creative and advanced than the chatbots. Through this investment, the company will accelerate breakthroughs in AI, and both companies will commercialize advanced technologies., November 2022 - Tntra and Invenio have partnered to develop a platform that offers comprehensive data analysis on a firm. Throughout the process, Tntra offered complete engineering support and cooperation to Invenio. Tantra offers feeds, knowledge graphs, intelligent text extraction, and analytics, which enables Invenio to give information on seven parts of the business, such as false news identification, subject categorization, dynamic data extraction, article summaries, sentiment analysis, and keyword extraction.. Key drivers for this market are: Growing Demand for Social Media Analytics, Rising Practice of Predictive Analytics. Potential restraints include: Growing Demand for Social Media Analytics, Rising Practice of Predictive Analytics. Notable trends are: Retail and E-commerce to Hold a Significant Share in Text Analytics Market.

Simulations inform all aspects of modern astrophysical research, ranging in scale from 1D and 2D test problems that can run in seconds on an astronomer's laptop all the way to large-scale 3D calculations that run on the largest supercomputers, with a spectrum of data sizes and shapes filling the landscape between these two extremes. In this talk I will review the diversity of astrophysics simulation data formats commonly in use by researchers, providing an overview of the most common simulation techniques, including pure N-body dynamics, smoothed particle hydrodynamics (SPH), adaptive mesh refinement (AMR), spectral methods, and unstructured meshes. Additionally, I will highlight methods for incorporating physical phenomena that are important for astrophysics, including chemistry, magnetic fields, radiative transport, and "subgrid" recipes for important physics that cannot be directly resolved in a simulation. In addition to the numerical techniques, I will also discuss the communities that have developed around these simulation codes and argue that increasing use and availability of open community codes has dramatically lowered the barrier to entry for novice simulators. Extracting scientific results from astrophysical simulation data requires detailed knowledge of the underlying data structures and data formats, as well as the semantic meaning of the data in relation to the physics problem posed by the simulation. As a solution to this problem, I will present yt, a community-developed python library for analyzing and visualizing simulation data. With support for most of the common astrophysics simulation research data formats, yt endeavors to provide a universal language for asking physically motivated questions of simulation data, regardless of the underlying data format. I will highlight the community of yt contributors and users, showcase scientific results where yt was used to facilitate the analysis.

Knowledge Extraction Software Market Outlook

According to our latest research, the global Knowledge Extraction Software market size reached USD 4.8 billion in 2024, driven by the escalating demand for advanced data analytics and automation solutions across diverse industries. The market is experiencing robust growth, with a recorded CAGR of 19.2% from 2025 to 2033. By the end of this forecast period, the Knowledge Extraction Software market is projected to attain a value of USD 20.5 billion by 2033. This impressive upward trajectory is primarily fueled by the proliferation of big data, the integration of artificial intelligence (AI) technologies, and the urgent need for organizations to derive actionable insights from ever-expanding data sources.

One of the primary growth factors propelling the Knowledge Extraction Software market is the exponential rise in unstructured data generated by businesses and individuals. Organizations are increasingly challenged by the sheer volume, velocity, and variety of data, which often remains untapped in the absence of sophisticated extraction tools. Knowledge extraction software enables enterprises to efficiently mine, process, and analyze unstructured data from various sources such as emails, documents, social media, and multimedia files. The adoption of these solutions is further accelerated by the growing emphasis on data-driven decision-making and digital transformation initiatives. Enterprises across sectors like BFSI, healthcare, and retail are leveraging knowledge extraction software to enhance operational efficiency, improve customer experience, and gain a competitive edge in the market.

Another significant driver for the Knowledge Extraction Software market is the rapid advancement of AI and machine learning technologies. Modern knowledge extraction tools are now equipped with advanced algorithms that can perform complex tasks such as natural language processing (NLP), sentiment analysis, entity recognition, and image/video analysis. These capabilities empower organizations to extract deeper insights and automate routine processes, thereby reducing manual effort and operational costs. Furthermore, the integration of cloud computing has democratized access to sophisticated knowledge extraction platforms, enabling even small and medium-sized enterprises (SMEs) to harness the power of AI-driven analytics without significant upfront investments in infrastructure.

The increasing regulatory focus on data privacy and compliance is also shaping the trajectory of the Knowledge Extraction Software market. With regulations such as GDPR, CCPA, and HIPAA becoming more stringent, organizations are compelled to implement robust data governance frameworks. Knowledge extraction software plays a pivotal role in ensuring compliance by automating the identification, classification, and protection of sensitive information. This not only mitigates the risk of data breaches but also enhances transparency and accountability in data management practices. As a result, industries with high regulatory scrutiny, such as finance, healthcare, and government, are witnessing accelerated adoption of knowledge extraction solutions.

From a regional perspective, North America currently dominates the Knowledge Extraction Software market, accounting for the largest revenue share in 2024. This leadership position is attributed to the presence of leading technology providers, rapid digitalization across industries, and significant investments in AI and big data analytics. However, the Asia Pacific region is expected to register the fastest growth during the forecast period, driven by the digital transformation wave sweeping across emerging economies like China, India, and Southeast Asia. The increasing adoption of cloud-based solutions and the expansion of IT infrastructure in these regions are creating lucrative opportunities for market players. Meanwhile, Europe is also witnessing notable growth, particularly in sectors such as BFSI and healthcare, where compliance and data security are paramount.

Component Analysis

The Knowledge Extraction Software market is segmented by component into software and services, each playing a crucial role in the value chain. The software segment, which includes standalone knowledge extraction platforms and integrated analytics solutions, currently holds the largest market share. This dominance is largely due to the continuous evolution of AI-powered algorithms and the increasing integrat

The size of the Walnut Hull Extract market was valued at USD XXX million in 2023 and is projected to reach USD XXX million by 2032, with an expected CAGR of XX% during the forecast period.

We have successfully extracted a comprehensive news dataset from CNBC, covering not only financial updates but also an extensive range of news categories relevant to diverse audiences in Europe, the US, and the UK. This dataset includes over 500,000 records, meticulously structured in JSON format for seamless integration and analysis.

Diverse News Segments for In-Depth Analysis

This extensive extraction spans multiple segments, such as:

• Business and Market Analysis: Stay updated on major companies, mergers, and acquisitions.
• Technology and Innovation: Explore developments in AI, cybersecurity, and digital transformation.
• Economic Forecasts: Access insights into GDP, employment rates, inflation, and other economic indicators.
• Geopolitical Developments: Understand the impact of political events and global trade dynamics on markets.
• Personal Finance: Learn about saving strategies, investment tips, and real estate trends.

Each record in the dataset is enriched with metadata tags, enabling precise filtering by region, sector, topic, and publication date.

Why Choose This Dataset?

The comprehensive news dataset provides real-time insights into global developments, corporate strategies, leadership changes, and sector-specific trends. Designed for media analysts, research firms, and businesses, it empowers users to perform:

• Trend Analysis
• Sentiment Analysis
• Predictive Modeling

Additionally, the JSON format ensures easy integration with analytics platforms for advanced processing.

Access More News Datasets

Looking for a rich repository of structured news data? Visit our news dataset collection to explore additional offerings tailored to your analysis needs.

Sample Dataset Available

To get a preview, check out the CSV sample of the CNBC economy articles dataset.

A crucial part of sentiment classification is featuring extraction because it involves extracting valuable information from text data, which affects the model’s performance. The goal of this paper is to help in selecting a suitable feature extraction method to enhance the performance of sentiment analysis tasks. In order to provide directions for future machine learning and feature extraction research, it is important to analyze and summarize feature extraction techniques methodically from a machine learning standpoint. There are several methods under consideration, including Bag-of-words (BOW), Word2Vector, N-gram, Term Frequency- Inverse Document Frequency (TF-IDF), Hashing Vectorizer (HV), and Global vector for word representation (GloVe). To prove the ability of each feature extractor, we applied it to the Twitter US airlines and Amazon musical instrument reviews datasets. Finally, we trained a random forest classifier using 70% of the training data and 30% of the testing data, enabling us to evaluate and compare the performance using different metrics. Based on our results, we find that the TD-IDF technique demonstrates superior performance, with an accuracy of 99% in the Amazon reviews dataset and 96% in the Twitter US airlines dataset. This study underscores the paramount significance of feature extraction in sentiment analysis, endowing pragmatic insights to elevate model performance and steer future research pursuits.

Global Lithium Extraction from Salt Lake Brine Market Report 2024 comes with the extensive industry analysis of development components, patterns, flows and sizes. The report also calculates present and past market values to forecast potential market management through the forecast period between 2024-2030. The report may be the best of what is a geographic area which expands the competitive landscape and industry perspective of the market.