자연어 처리(NLP) 시장 보고서 : 유형별, 기술별, 전개 방식별, 조직 규모별, 최종사용자별, 지역별(2026-2034년)

The global natural language processing (NLP) market size reached USD 34.4 Billion in 2025. Looking forward, IMARC Group expects the market to reach USD 186.4 Billion by 2034, exhibiting a growth rate (CAGR) of 20.02% during 2026-2034. The market is primarily driven by the growing e-commerce sector, the expanding use of natural language processing in the healthcare industry, and the growing acceptance of artificial intelligence and machine learning technologies across the globe.

NATURAL LANGUAGE PROCESSING MARKET ANALYSIS:

• Major Market Drivers: Data extraction is driving the market growth for natural language processing (NLP) solutions. Other factors include sentiment analysis, deep learning algorithms, the growing need for automated language processing in customer service, and rising demand for artificial intelligence (AI)-powered solutions.
• Key Market Trends: The development of sentiment analysis and contextual understanding to extract deeper insights from textual material, and the fusion of NLP with other technologies like machine translation and speech recognition to achieve full language comprehension is contributing to the market growth. Also, the natural language processing market overview presents insightful information about the new low-code and no-code NLP technologies that are democratizing language processing capabilities and encouraging wider usage.
• Geographic Trends: North America continues to dominate the NLP industry due to the early adoption of AI technology and the existence of large tech businesses. Additionally, Europe is growing steadily due to the growing use of NLP in the government, banking, and healthcare sectors, while Asia Pacific is driven by expanding investments in AI research and development, particularly in nations like China and India.
• Competitive Landscape: Some of the major natural language processing companies include Addepto sp. z o.o., Baidu, C5i, Consensus Cloud Solutions, Inc., Conversica, Inc, Gnani Innovations Private Limited, Google LLC, International Business Machines Corporation, IQVIA Inc, Linguamatics, SoundHound AI Inc, Verint Systems Inc. and Veritone, Inc. among many others.
• Challenges and Opportunities: The market expansion and acceptance are challenged by ethical worries about data privacy, bias in algorithms, and exploitation of NLP technology. Additionally, businesses using NLP technologies face challenges due to integration complexity and the requirement for domain-specific customization. The natural language processing recent opportunities, include NLP for improved decision-making, individualized consumer experiences, and the automation of repetitive work in a variety of sectors.

NATURAL LANGUAGE PROCESSING MARKET TRENDS:

Increasing Adoption of AI and Machine Learning Technologies

The natural language processing market is growing with the increasing use of artificial intelligence (AI) and machine learning (ML) technologies. Additionally, NLP technologies are proficient at tasks like sentiment analysis, translation, and personal assistants due to AI and machine learning (ML) algorithms, enabling them to learn from data inputs and improve over time without human interaction. For instance, new Horizon Europe funding strengthens European research in AI and quantum technology under the 2023-2024 Horizon Europe Digital, Industry, and Space work program. A new series of calls has been released, providing approximately €112 million in funding for creative initiatives. Additionally, the European Commission opened new financing avenues and committed over €112 million to promote innovative initiatives in quantum and artificial intelligence (AI) through Horizon Europe. Moreover, €50 million portion of the funds is designated for the development of large-scale AI models. The purpose of these improvements is to expand the models' functionality and support additional data types to create generative AI systems that can process and produce multimodal data, including text, photos, audio, video, and 3D models. These systems are produced to adjust to different settings and jobs. Hence, as businesses continue to focus on digital transformation, the reliance on data-driven decision-making further drives the adoption of NLP technologies, thus positively generating natural language processing market revenue.

E-commerce Growth

The global e-commerce market was valued at US$ 21.1 trillion as per the IMARC Group. It is also projected to grow to US$ 183.8 trillion by 2032, with an anticipated compound annual growth rate (CAGR) of 27.16% from 2024 to 2032. The e-commerce sector adopts natural language processing market growth for improving customer experience and optimizing their operations. These technologies are employed to understand and reply to client questions, automate customer support, and provide more user-friendly search functions. E-commerce companies may analyze vast amounts of client data, comprehend consumer behavior, and offer tailored shopping experiences by utilizing NLP to retain and satisfy customers. Moreover, NLP technologies are also used for sentiment analysis, which aids businesses in determining customer thoughts and comments on goods and services which allows for more focused marketing campaigns and product modifications. Thus, NLP has completely changed how e-commerce websites work, allowing customers to locate items using ordinary language, which increases user engagement and boosts sales conversions.

Increasing Adoption in the Healthcare Sector

The natural language processing (NLP) industry is growing with the healthcare sector's increasing dependence on these technologies. NLP makes it possible to analyze and handle enormous volumes of unstructured clinical data such as patient feedback, medical research articles, and electronic health records (EHRs) efficiently. According to the World Economic Forum AI spending in India will amount to $11.78 billion by 2025, and by 2035, it will contribute $1 trillion to the GDP of the country. Moreover, as per the National Institutes of Health (NIH) in an April 2023 published article, artificial intelligence is transforming clinical research and enhancing data quality. The aggregation and analysis of extensive clinical and biological data enhance patient enrollment, engagement, trial efficiency, and the quality of outcomes. Additionally, robust clinical and molecular datasets have significantly improved the predictive modeling of pharmaceuticals and biological functions. Even the largest datasets are useless if individuals can't understand them well enough. The capacity to recognize important patterns and trends in health data is a must for analysts. Thus, software for natural language processing (NLP) becomes essential in this situation for narrative text detection, extraction, and systematization into structured data using machine learning (ML) in NLP to be used in research for simplifying procedures. For instance, as per Coursera, AI in healthcare is expected to be worth $208.2 billion in 2030, which is higher than its $15.4 billion market size in 2022. Thus, this growing investment and adoption in the healthcare sector underscores the pivotal role of NLP technologies, which is creating a positive natural language processing market outlook.

NATURAL LANGUAGE PROCESSING (NLP) MARKET SEGMENTATION:

Breakup by Type:

• Hardware
• Software
• Services

Hardware in the NLP market includes devices and components essential for processing and analyzing natural language data. It focuses on specialized processors that are developed to perform the large calculations needed for machine learning and language modeling, including GPUs and FPGAs. Additionally, servers and storage solutions are essential since they offer the infrastructure required to run NLP applications. Furthermore, the development of hardware technology improves the effectiveness and speed of NLP solutions, allowing large-scale implementation in a variety of industries, including customer service, banking, and healthcare, as well as real-time processing.

NLP software forms the core of NLP applications, encompassing technologies that enable machines to understand, interpret, and generate human language in a way that is meaningful and useful. It includes text analytics, speech recognition, machine translation, and sentiment analysis tools. It is essential for extracting insights from unstructured data and is incorporated into customer service applications, chatbots, and virtual assistants to enhance user interactions and improve service delivery. Moreover, ongoing developments in machine learning (ML) and artificial intelligence (AI) are continually expanding the capabilities of NLP software, making it more accurate and accessible for users across different sectors.

NLP services include a range of consulting, integration, and maintenance services provided by IT vendors to help organizations deploy and manage NLP solutions. These services are critical for businesses that lack the in-house expertise necessary to implement complex NLP systems. Additionally, service providers offer support from the initial setup to ongoing operations, ensuring that NLP tools are effectively integrated into the existing IT infrastructure and are aligned with the goals of the organization. It is crucial for customizing solutions to specific industry needs, thus enabling more effective communication, improved customer experiences, and enhanced decision-making processes through data-driven insights.

Breakup by Technology:

• Interactive Voice Response
• Optical Character Recognition
• Text Analytics
• Speech Analytics
• Classification and Categorization
• Pattern and Image Recognition
• Others

Text analytics holds the largest share of the industry

Text analytics offers extensive applications across diverse industries such as finance, healthcare, customer service, and marketing. It involves the process of converting unstructured text data into meaningful data for analysis, using different linguistic, statistical, and machine-learning (ML) techniques. This technology enables businesses to extract insights, patterns, and trends from large volumes of text, helping them to enhance customer experience, manage brand reputation, automate processes, and make data-driven decisions. On 21 June 2023, Lexalytics, a trailblazer in AI-based NLP technology and part of InMoment(R), was honored with Best Overall NLP Company at the sixth annual AI Breakthrough Awards. This event, organized by AI Breakthrough, an eminent market intelligence firm, honors outstanding achievements in the global AI sector. As Lexalytics celebrates two decades of innovation in machine learning and natural language processing, it stands out as a leading pioneer in NLP commercialization and in assisting businesses to interpret their unstructured data. Lexalytics enables clients to process text data in 31 native languages and dialects, covering about 70% of the global population across six continents.

Breakup by Deployment Mode:

• On-premises
• Cloud-based

On-premises represents the leading market segment

On-premises deployment can be attributed to the enhanced security and control that on-premises solutions offer, making them a preferred choice for organizations dealing with sensitive data or requiring stringent data control and regulatory compliance. Additionally, on-premises deployment allows companies to customize their NLP solutions to fit specific business needs and integrate seamlessly with existing IT infrastructure, thus surging natural language processing demand. For instance, on 1 November 2023, Nokia introduced Nokia Bell Labs, a pioneering research development named natural-language networks, enabling network operations via simple spoken or written commands. These networks will comprehend user intentions and autonomously respond to them. Natural-language networks simplify network management and enhance responsiveness to user demands. These networks can instantly provide and adjust to the optimal network configuration for any request by a service provider utilizing AI.

Breakup by Organization Size:

• Large Enterprises
• Small and Medium-sized Enterprises

Small and medium-sized enterprises exhibit a clear dominance in the market

Small and medium-sized enterprises (SMEs) are widely adopting NLP technologies to enhance operational efficiencies and customer experience without the substantial resource allocation typical of larger corporations. They employ NLP tools for a variety of applications, including customer service automation, sentiment analysis, and data analytics, which provide significant competitive advantages. The scalability and relative affordability of NLP solutions have democratized access, enabling these smaller entities to implement sophisticated technologies that were previously the domain of larger organizations, thereby driving substantial growth within this segment. Besides this, in May 2023, Xdroid unveiled two innovative features based on natural language processing (NLP) such as auto-summary and topic detection/insight. The integration of NLP technologies enhances the functionality of their VoiceAnalytics product by boosting its AI capabilities. This enhancement allows the system to automatically understand and analyze human language, thereby minimizing the need for manual configuration and enhancing the insights delivered to customers.

Breakup by End User:

• Education
• BFSI
• Healthcare
• IT and Telecom
• Manufacturing and Retail
• Media and Entertainment
• Others

IT and Telecom dominate the market

As per the natural language processing market forecast, the IT and telecom segment is dominating the market growth. Additionally, the increasing adoption of NLP technologies within these industries to enhance customer experience, automate service desks and optimize network operations. Companies in the IT and telecom sectors are using NLP for sentiment analysis, customer service bots, and real-time communication analytics, which streamline operations, and significantly improve customer satisfaction and retention. For instance, in February 2024, Microsoft introduced Azure OpenAI service within Azure Government. This addition complements Microsoft's existing suite of AI services, which includes Azure AI services (previously Azure cognitive services), Azure machine learning, and Azure AI search, to bolster AI capabilities in government sectors. Moreover, Azure OpenAI service within Azure government allows agencies with strict security and compliance needs to deploy this top-tier generative AI service at an unclassified level.

Breakup by Region:

• North America
  • United States
  • Canada
• Asia-Pacific
  • China
  • Japan
  • India
  • South Korea
  • Australia
  • Indonesia
  • Others
• Europe
  • Germany
  • France
  • United Kingdom
  • Italy
  • Spain
  • Russia
  • Others
• Latin America
  • Brazil
  • Mexico
  • Others
• Middle East and Africa

North America leads the market, accounting for the largest natural language processing (NLP) market share

The report has also provided a comprehensive analysis of all the major regional markets, which include North America (the United States and Canada); Europe (Germany, France, the United Kingdom, Italy, Spain, Russia, and others); Asia Pacific (China, Japan, India, South Korea, Australia, Indonesia, and others); Latin America (Brazil, Mexico, and others); and the Middle East and Africa. According to the report, North America represents the largest regional market for natural language processing (NLP).

As per the natural language processing recent development North America is emerging as the dominant segment. This is attributed to the region's robust technological infrastructure, strong presence of leading technology giants, and significant investments in AI and machine learning technologies. Additionally, the growing emphasis on enhancing user experience and the widespread adoption of advanced analytics in industries such as healthcare, finance, and customer service further propel the market growth. For instance, in February 2023, Amazon Web Services (AWS) formed a partnership with Hugging Face, a provider of natural language processing (NLP) models, to improve the training and implementation speeds of extensive language models. Hugging Face and AWS collaborated to integrate over 7,000 NLP models into Amazon SageMaker, enhancing inference speed and distributed training capabilities.

COMPETITIVE LANDSCAPE:

The key players in the market are actively enhancing market growth through a variety of strategic initiatives. These companies are heavily investing in research and development to innovate and improve their NLP solutions, making them more accurate, efficient, and adaptable to different languages and dialects. They are also forming strategic partnerships and collaborations with other tech companies to integrate NLP more seamlessly into various applications, such as customer service bots, real-time translation services, and healthcare diagnostics tools. Furthermore, leading firms are acquiring startups and smaller companies to diversify their capabilities and expand their technological reach. For instance, on 7 February 2023, Expert.ai, a prominent key player in artificial intelligence (AI) for language comprehension and operations, unveiled new enhancements to its natural language (NL) platform. These updates improve the support for specialized natural language processing (NLP) workflows. It utilizes a hybrid methodology that integrates the latest in NL methods such as machine learning (ML) and symbolic AI based on knowledge. The expert.ai Platform maximizes the utility of unstructured data found in documents, applications, and tools and enables organizations to develop innovative business models, expedite the realization of value, and refine operational processes.

The report provides a comprehensive analysis of the competitive landscape in the global natural language processing (NLP) market with detailed profiles of all major companies, including:

• Addepto sp. z o.o.
• Baidu
• C5i
• Consensus Cloud Solutions, Inc.
• Conversica, Inc
• Gnani Innovations Private Limited
• Google LLC
• International Business Machines Corporation
• IQVIA Inc
• Linguamatics
• SoundHound AI Inc
• Verint Systems Inc.
• Veritone, Inc.

KEY QUESTIONS ANSWERED IN THIS REPORT

1. What was the size of the global natural language processing (NLP) market in 2025?

2. What is the expected growth rate of the global natural language processing (NLP) market during 2026-2034?

3. What are the key factors driving the global natural language processing (NLP) market?

4. What has been the impact of COVID-19 on the global natural language processing (NLP) market?

5. What is the breakup of the global natural language processing (NLP) market based on the technology?

6. What is the breakup of the global natural language processing (NLP) market based on the deployment mode?

7. What is the breakup of the global natural language processing (NLP) market based on the organization size?

8. What is the breakup of the global natural language processing (NLP) market based on the end user?

9. What are the key regions in the global natural language processing (NLP) market?

10. Who are the key players/companies in the global natural language processing (NLP) market?

Table of Contents

1 Preface

2 Scope and Methodology

• 2.1 Objectives of the Study
• 2.2 Stakeholders
• 2.3 Data Sources
  • 2.3.1 Primary Sources
  • 2.3.2 Secondary Sources
• 2.4 Market Estimation
  • 2.4.1 Bottom-Up Approach
  • 2.4.2 Top-Down Approach
• 2.5 Forecasting Methodology

3 Executive Summary

4 Introduction

• 4.1 Overview
• 4.2 Key Industry Trends

5 Global Natural Language Processing (NLP) Market

• 5.1 Market Overview
• 5.2 Market Performance
• 5.3 Impact of COVID-19
• 5.4 Market Forecast

6 Market Breakup by Type

• 6.1 Hardware
  • 6.1.1 Market Trends
  • 6.1.2 Market Forecast
• 6.2 Software
  • 6.2.1 Market Trends
  • 6.2.2 Market Forecast
• 6.3 Services
  • 6.3.1 Market Trends
  • 6.3.2 Market Forecast

7 Market Breakup by Technology

• 7.1 Interactive Voice Response
  • 7.1.1 Market Trends
  • 7.1.2 Market Forecast
• 7.2 Optical Character Recognition
  • 7.2.1 Market Trends
  • 7.2.2 Market Forecast
• 7.3 Text Analytics
  • 7.3.1 Market Trends
  • 7.3.2 Market Forecast
• 7.4 Speech Analytics
  • 7.4.1 Market Trends
  • 7.4.2 Market Forecast
• 7.5 Classification and Categorization
  • 7.5.1 Market Trends
  • 7.5.2 Market Forecast
• 7.6 Pattern and Image Recognition
  • 7.6.1 Market Trends
  • 7.6.2 Market Forecast
• 7.7 Others
  • 7.7.1 Market Trends
  • 7.7.2 Market Forecast

8 Market Breakup by Deployment Mode

• 8.1 On-premises
  • 8.1.1 Market Trends
  • 8.1.2 Market Forecast
• 8.2 Cloud-based
  • 8.2.1 Market Trends
  • 8.2.2 Market Forecast

9 Market Breakup by Organization Size

• 9.1 Large Enterprises
  • 9.1.1 Market Trends
  • 9.1.2 Market Forecast
• 9.2 Small and Medium-sized Enterprises
  • 9.2.1 Market Trends
  • 9.2.2 Market Forecast

10 Market Breakup by End User

• 10.1 Education
  • 10.1.1 Market Trends
  • 10.1.2 Market Forecast
• 10.2 BFSI
  • 10.2.1 Market Trends
  • 10.2.2 Market Forecast
• 10.3 Healthcare
  • 10.3.1 Market Trends
  • 10.3.2 Market Forecast
• 10.4 IT and Telecom
  • 10.4.1 Market Trends
  • 10.4.2 Market Forecast
• 10.5 Manufacturing and Retail
  • 10.5.1 Market Trends
  • 10.5.2 Market Forecast
• 10.6 Media and Entertainment
  • 10.6.1 Market Trends
  • 10.6.2 Market Forecast
• 10.7 Others
  • 10.7.1 Market Trends
  • 10.7.2 Market Forecast

11 Market Breakup by Region

• 11.1 North America
  • 11.1.1 United States
    • 11.1.1.1 Market Trends
    • 11.1.1.2 Market Forecast
  • 11.1.2 Canada
    • 11.1.2.1 Market Trends
    • 11.1.2.2 Market Forecast
• 11.2 Asia-Pacific
  • 11.2.1 China
    • 11.2.1.1 Market Trends
    • 11.2.1.2 Market Forecast
  • 11.2.2 Japan
    • 11.2.2.1 Market Trends
    • 11.2.2.2 Market Forecast
  • 11.2.3 India
    • 11.2.3.1 Market Trends
    • 11.2.3.2 Market Forecast
  • 11.2.4 South Korea
    • 11.2.4.1 Market Trends
    • 11.2.4.2 Market Forecast
  • 11.2.5 Australia
    • 11.2.5.1 Market Trends
    • 11.2.5.2 Market Forecast
  • 11.2.6 Indonesia
    • 11.2.6.1 Market Trends
    • 11.2.6.2 Market Forecast
  • 11.2.7 Others
    • 11.2.7.1 Market Trends
    • 11.2.7.2 Market Forecast
• 11.3 Europe
  • 11.3.1 Germany
    • 11.3.1.1 Market Trends
    • 11.3.1.2 Market Forecast
  • 11.3.2 France
    • 11.3.2.1 Market Trends
    • 11.3.2.2 Market Forecast
  • 11.3.3 United Kingdom
    • 11.3.3.1 Market Trends
    • 11.3.3.2 Market Forecast
  • 11.3.4 Italy
    • 11.3.4.1 Market Trends
    • 11.3.4.2 Market Forecast
  • 11.3.5 Spain
    • 11.3.5.1 Market Trends
    • 11.3.5.2 Market Forecast
  • 11.3.6 Russia
    • 11.3.6.1 Market Trends
    • 11.3.6.2 Market Forecast
  • 11.3.7 Others
    • 11.3.7.1 Market Trends
    • 11.3.7.2 Market Forecast
• 11.4 Latin America
  • 11.4.1 Brazil
    • 11.4.1.1 Market Trends
    • 11.4.1.2 Market Forecast
  • 11.4.2 Mexico
    • 11.4.2.1 Market Trends
    • 11.4.2.2 Market Forecast
  • 11.4.3 Others
    • 11.4.3.1 Market Trends
    • 11.4.3.2 Market Forecast
• 11.5 Middle East and Africa
  • 11.5.1 Market Trends
  • 11.5.2 Market Breakup by Country
  • 11.5.3 Market Forecast

12 SWOT Analysis

• 12.1 Overview
• 12.2 Strengths
• 12.3 Weaknesses
• 12.4 Opportunities
• 12.5 Threats

13 Value Chain Analysis

14 Porters Five Forces Analysis

• 14.1 Overview
• 14.2 Bargaining Power of Buyers
• 14.3 Bargaining Power of Suppliers
• 14.4 Degree of Competition
• 14.5 Threat of New Entrants
• 14.6 Threat of Substitutes

15 Price Analysis

16 Competitive Landscape

• 16.1 Market Structure
• 16.2 Key Players
• 16.3 Profiles of Key Players
  • 16.3.1 Addepto sp. z o.o.
    • 16.3.1.1 Company Overview
    • 16.3.1.2 Product Portfolio
    • 16.3.1.3 Financials
    • 16.3.1.4 SWOT Analysis
  • 16.3.2 Baidu
    • 16.3.2.1 Company Overview
    • 16.3.2.2 Product Portfolio
    • 16.3.2.3 Financials
    • 16.3.2.4 SWOT Analysis
  • 16.3.3 C5i
    • 16.3.3.1 Company Overview
    • 16.3.3.2 Product Portfolio
    • 16.3.3.3 Financials
    • 16.3.3.4 SWOT Analysis
  • 16.3.4 Consensus Cloud Solutions, Inc.
    • 16.3.4.1 Company Overview
    • 16.3.4.2 Product Portfolio
    • 16.3.4.3 Financials
    • 16.3.4.4 SWOT Analysis
  • 16.3.5 Conversica, Inc
    • 16.3.5.1 Company Overview
    • 16.3.5.2 Product Portfolio
    • 16.3.5.3 Financials
    • 16.3.5.4 SWOT Analysis
  • 16.3.6 Gnani Innovations Private Limited
    • 16.3.6.1 Company Overview
    • 16.3.6.2 Product Portfolio
    • 16.3.6.3 Financials
    • 16.3.6.4 SWOT Analysis
  • 16.3.7 Google LLC
    • 16.3.7.1 Company Overview
    • 16.3.7.2 Product Portfolio
    • 16.3.7.3 Financials
    • 16.3.7.4 SWOT Analysis
  • 16.3.8 International Business Machines Corporation
    • 16.3.8.1 Company Overview
    • 16.3.8.2 Product Portfolio
    • 16.3.8.3 Financials
    • 16.3.8.4 SWOT Analysis
  • 16.3.9 IQVIA Inc
    • 16.3.9.1 Company Overview
    • 16.3.9.2 Product Portfolio
    • 16.3.9.3 Financials
    • 16.3.9.4 SWOT Analysis
  • 16.3.10 Linguamatics
    • 16.3.10.1 Company Overview
    • 16.3.10.2 Product Portfolio
    • 16.3.10.3 Financials
    • 16.3.10.4 SWOT Analysis
  • 16.3.11 SoundHound AI Inc
    • 16.3.11.1 Company Overview
    • 16.3.11.2 Product Portfolio
    • 16.3.11.3 Financials
    • 16.3.11.4 SWOT Analysis
  • 16.3.12 Verint Systems Inc.
    • 16.3.12.1 Company Overview
    • 16.3.12.2 Product Portfolio
    • 16.3.12.3 Financials
    • 16.3.12.4 SWOT Analysis
  • 16.3.13 Veritone, Inc.
    • 16.3.13.1 Company Overview
    • 16.3.13.2 Product Portfolio
    • 16.3.13.3 Financials
    • 16.3.13.4 SWOT Analysis