자연언어처리(NLP) 시장(-2035년) : 컴포넌트 유형별, 처리 유형별, 배포 유형별, 용도 유형별, 최종사용자별, 지역별, 업계 동향, 예측

Natural Language Processing Market, Till 2035: Distribution by Type of Component, Type of Processing, Type of Deployment, Type of Application, Type of End User, and Geographical Regions: Industry Trends and Global Forecasts

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한글목차

자연언어처리 시장 개요

세계의 자연언어처리(NLP) 시장 규모는 현재 259억 8,000만 달러에서 2035년까지 3,024억 달러에 달할 것으로 추정되며, 2035년까지의 예측 기간에 CAGR로 25%의 성장이 전망됩니다.

자연 언어 처리 시장 : 성장과 동향

자연 언어 처리(NLP)는 컴퓨터 과학과 AI의 한 분야로, 컴퓨터가 인간의 언어를 이해하고 대화할 수 있도록 하는 분야입니다. 이 분야는 계산언어학과 머신러닝, 딥러닝 기술을 융합하여 방대한 양의 자연 언어 데이터를 분석함으로써 컴퓨터가 텍스트와 음성을 인식, 이해, 생성하는 능력을 향상시키는 분야입니다.

자연 언어 처리의 미래는 문맥 파악, 음성 인식, 자연 언어 이해, 기계 번역 촉진과 같은 능력의 향상으로 특징지을 수 있습니다. 이러한 발전의 결과, NLP 기술은 사용자들 사이에서 인기를 끌며 다양한 분야에서 AI 챗봇 서비스 이용을 촉진하고 있습니다. 또한 이러한 주요 기능은 기업이 업무를 최적화하고 자동화하여 직원의 생산성을 높이고 복잡한 업무 프로세스를 단순화할 수 있도록 도와줍니다. 최근 NLP 시장 동향을 보면 의료, 금융, E-Commerce를 포함한 여러 산업에서 AI 솔루션에 대한 수요가 크게 증가하고 있음을 알 수 있습니다. 그 결과, 주요 기업은 경쟁 우위를 유지하기 위해 연구개발에 많은 투자를 하고 있으며, 감정 분석, 챗봇, 고급 번역 서비스 등 혁신적인 용도를 창출하고 있습니다.

이러한 모멘텀은 현대 비즈니스 전략에서 NLP의 중요성을 강력하게 보여줄 뿐만 아니라, 이 기술을 채택하고자 하는 조직에게 유망한 기회를 보여줍니다. 머신러닝 용도의 지속적인 기술 발전, 디지털 전환의 부상, 음성 지원 기기의 보급에 힘입어 자연 언어 처리 시장은 예측 기간 중 큰 폭으로 성장할 것으로 예측됩니다.

세계의 자연언어처리 시장에 대해 조사했으며, 시장 규모 추산과 기회의 분석, 경쟁 구도, 기업 개요 등의 정보를 제공하고 있습니다.

목차

섹션 1 리포트 개요

제1장 서문

제2장 조사 방법

제3장 시장 역학

제4장 거시경제 지표

섹션 2 정성적 인사이트

제5장 개요

제6장 서론

제7장 규제 시나리오

섹션 3 시장 개요

제8장 주요 기업의 종합적 데이터베이스

제9장 경쟁 구도

제10장 화이트 스페이스 분석

제11장 기업 경쟁력 분석

제12장 자연언어처리 시장의 스타트업 에코시스템

섹션 4 기업 개요

제13장 기업 개요

• 챕터 개요
• 3M
• Amazon
• Apple
• Baidu
• Crayon Data
• Google
• Hewlett Packard
• IBM
• Inbenta Holding
• IQVIA
• Just AI
• Linguamatics
• Meta Platforms
• Microsoft
• NetBase Quid
• Open AI
• Oracle
• Rasa
• Rasa
• SAP
• SAS
• SoundHound AI

섹션 5 시장 동향

제14장 메가트렌드 분석

제15장 미충족 요구 분석

제16장 특허 분석

제17장 최근 발전

섹션 6 시장 기회 분석

제18장 세계의 자연언어처리 시장

제19장 시장 기회 : 컴포넌트 유형별

제20장 시장 기회 : 처리 유형별

제21장 시장 기회 : 배포 유형별

제22장 시장 기회 : 용도 유형별

제23장 시장 기회 : 최종사용자 유형별

제24장 북미의 자연언어처리의 시장 기회

제25장 유럽의 자연언어처리의 시장 기회

제26장 아시아의 자연언어처리의 시장 기회

제27장 중동·북아프리카(MENA) 자연언어처리의 시장 기회

제28장 라틴아메리카의 자연언어처리의 시장 기회

제29장 기타 지역의 자연언어처리의 시장 기회

제30장 시장 집중 분석 : 주요 기업별

제31장 인접 시장 분석

섹션 7 전략적 툴

제32장 주요 승리 전략

제33장 Porter's Five Forces 분석

제34장 SWOT 분석

제35장 밸류체인 분석

제36장 Roots의 전략적 제안

섹션 8 기타 독점적 인사이트

제37장 1차 조사로부터의 인사이트

제38장 리포트 결론

섹션 9 부록

KSA

영문 목차

영문목차

Natural Language Processing Market Overview

As per Roots Analysis, the global natural language processing market size is estimated to grow from USD 25.98 billion in the current year USD 302.4 billion by 2035, at a CAGR of 25% during the forecast period, till 2035.

The opportunity for natural language processing market has been distributed across the following segments:

Type of Component

• Solution
• Service

Type of Processing

• Hybrid NLP
• Rule Based NLP
• Statistical NLP

Type of Deployment

• Cloud-based
• On-Premises

Type of Application

• Customer Experience Management
• Machine Translation
• Sentiment Analysis
• Social Media Monitoring
• Text Classification & Summarization
• Virtual Assistants / Chatbots
• Others

Type of End User

• BFSI
• Education
• Healthcare
• IT& Telecom
• Manufacturing
• Media & Entertainment
• Retail & E-Commerce
• Others

Geographical Regions

• North America
• US
• Canada
• Mexico
• Other North American countries
• Europe
• Austria
• Belgium
• Denmark
• France
• Germany
• Ireland
• Italy
• Netherlands
• Norway
• Russia
• Spain
• Sweden
• Switzerland
• UK
• Other European countries
• Asia
• China
• India
• Japan
• Singapore
• South Korea
• Other Asian countries
• Latin America
• Brazil
• Chile
• Colombia
• Venezuela
• Other Latin American countries
• Middle East and North Africa
• Egypt
• Iran
• Iraq
• Israel
• Kuwait
• Saudi Arabia
• UAE
• Other MENA countries
• Rest of the World
• Australia
• New Zealand
• Other countries

Natural Language Processing Market: Growth and Trends

Natural language processing (NLP) is a discipline within computer science and artificial intelligence that enables computers to comprehend and engage with human language. This field merges computational linguistics with machine learning and deep learning techniques to analyze vast quantities of natural language data, enhancing computers' abilities to recognize, understand, and generate text and speech.

The future of natural language processing is characterized by its growing capacity to grasp context, recognize speech, understand natural language, and facilitate machine translation. As a result of these advancements, NLP technology is gaining popularity among users and fostering the use of AI-driven chatbot services in a variety of sectors. Moreover, these key features assist companies in optimizing and automating their operations, which increases employee productivity and simplifies intricate business procedures. Recent trends in the NLP market reveal a significant rise in the demand for AI-driven solutions across multiple industries, including healthcare, finance, and e-commerce. Consequently, leading companies are heavily investing in research and development to maintain their competitive edge, resulting in innovative applications like sentiment analysis, chatbots, and enhanced translation services.

This momentum not only underscores the significance of NLP in contemporary business strategies but also points to promising opportunities for organizations eager to adopt this technology. Driven by the ongoing technological advancements in machine learning applications, the rise of digital transformation, and the increasing popularity of voice-enabled devices, the natural language processing market is expected to grow significantly during the forecast period.

Natural Language Processing Market: Key Segments

Market Share by Type of Component

Based on type of component, the global natural language processing market is segmented into solution and service. According to our estimates, currently, the solution segment captures the majority of the market share. This growth can be attributed to the increasing adoption of NLP solutions across diverse enterprises to automate processes and analyze information.

However, the service segment is expected to grow at a higher CAGR during the forecast period. This can be attributed to the growing need for integration of NLP solutions, maintenance services, and language translation services across different industries.

Market Share by Type of Processing

Based on type of processing, the global natural language processing market is segmented into hybrid NLP, rule-based NLP, and statistical NLP. According to our estimates, currently, the statistical NLP segment captures the majority of the market share. This can be attributed to its effectiveness in analyzing and automating data extraction methods. However, the hybrid NLP segment is expected to grow at a higher CAGR during the forecast period. This can be attributed to its ability to combine machine learning algorithms with established linguistic rule sets, enhancing both accuracy and flexibility.

Market Share by Type of Deployment

Based on type of deployment, the global natural language processing market is segmented into cloud-based and on-premises. According to our estimates, currently, cloud-based deployment captures the majority of the market share. The increased adoption of cloud-based NLP services is driven by their ease of integration, cost efficiency, and quick innovation and updates, contributing to market growth. Additionally, the availability of cloud services enables businesses to effectively implement natural language processing features such as chatbots and text analytics.

Market Share by Type of Application

Based on type of application, the global natural language processing market is segmented into customer experience management, machine translation, sentiment analysis, social media monitoring, text classification & summarization, virtual assistants / chatbots, and others. According to our estimates, currently, customer experience management captures the majority of the market share. This segment drives the demand for NLP solutions due to its diverse functionalities, such as virtual assistance, chatbots, and automation of customer service, which enhance interactions between customers and companies.

Market Share by Type of End User

Based on type of end user, the global natural language processing market is segmented into BFSI, education, healthcare, IT & telecom, manufacturing, media & entertainment, retail & e-commerce, and others. According to our estimates, currently, IT & telecom segment captures the majority of the market share. This can be attributed to the increasing requirement for enhanced customer support and improved user experience. However, the healthcare industry is expected to grow at a higher CAGR during the forecast period. This growth can be attributed to the increasing use of advanced predictive text technologies, software, and tools. Therefore, the growth of NLP within the healthcare field is expected to create numerous opportunities for market growth over the next decade.

Market Share by Geographical Regions

Based on geographical regions, the natural language processing market is segmented into North America, Europe, Asia, Latin America, Middle East and North Africa, and the rest of the world. According to our estimates, currently North America captures the majority share of the market. Additionally, the market in Asia is expected to grow at a higher CAGR during forecast period. This can be attributed to the increasing digital transformation across various sectors, which is driving the adoption of text analytics solutions, sentiment analysis tools, and automated customer support services.

Example Players in Natural Language Processing Market

• 3M
• Amazon
• Apple
• Baidu
• Crayon Data
• Google
• Health Fidelity
• Hewlett Packard
• IBM
• Inbenta Holding
• IQVIA
• Just AI Limited
• Linguamatics
• Meta Platform
• Microsoft
• NetBase Quid
• Open AI
• Oracle
• Rasa
• SAP
• SAS
• SoundHound AI

Natural Language Processing Market: Research Coverage

The report on the natural language processing market features insights on various sections, including:

• Market Sizing and Opportunity Analysis: An in-depth analysis of the natural language processing market, focusing on key market segments, including [A] type of component, [B] type of processing, [C] type of deployment, [D] type of application, [E] type of end user, and [F] geographical regions.
• Competitive Landscape: A comprehensive analysis of the companies engaged in the natural language processing market, based on several relevant parameters, such as [A] year of establishment, [B] company size, [C] location of headquarters and [D] ownership structure.
• Company Profiles: Elaborate profiles of prominent players engaged in the natural language processing market, providing details on [A] location of headquarters, [B] company size, [C] company mission, [D] company footprint, [E] management team, [F] contact details, [G] financial information, [H] operating business segments, [I] portfolio, [J] moat analysis, [K] recent developments, and an informed future outlook.
• Megatrends: An evaluation of ongoing megatrends in the natural language processing industry.
• Patent Analysis: An insightful analysis of patents filed / granted in the natural language processing domain, based on relevant parameters, including [A] type of patent, [B] patent publication year, [C] patent age and [D] leading players.
• Recent Developments: An overview of the recent developments made in the natural language processing market, along with analysis based on relevant parameters, including [A] year of initiative, [B] type of initiative, [C] geographical distribution and [D] most active players.
• Porter's Five Forces Analysis: An analysis of five competitive forces prevailing in the natural language processing market, including threats of new entrants, bargaining power of buyers, bargaining power of suppliers, threats of substitute products and rivalry among existing competitors.
• SWOT Analysis: An insightful SWOT framework, highlighting the strengths, weaknesses, opportunities and threats in the domain. Additionally, it provides Harvey ball analysis, highlighting the relative impact of each SWOT parameter.
• Value Chain Analysis: A comprehensive analysis of the value chain, providing information on the different phases and stakeholders involved in the natural language processing market.

Key Questions Answered in this Report

• How many companies are currently engaged in natural language processing market?
• Which are the leading companies in this market?
• What factors are likely to influence the evolution of this market?
• What is the current and future market size?
• What is the CAGR of this market?
• How is the current and future market opportunity likely to be distributed across key market segments?

Reasons to Buy this Report

• The report provides a comprehensive market analysis, offering detailed revenue projections of the overall market and its specific sub-segments. This information is valuable to both established market leaders and emerging entrants.
• Stakeholders can leverage the report to gain a deeper understanding of the competitive dynamics within the market. By analyzing the competitive landscape, businesses can make informed decisions to optimize their market positioning and develop effective go-to-market strategies.
• The report offers stakeholders a comprehensive overview of the market, including key drivers, barriers, opportunities, and challenges. This information empowers stakeholders to stay abreast of market trends and make data-driven decisions to capitalize on growth prospects.

Additional Benefits

• Complimentary Excel Data Packs for all Analytical Modules in the Report
• 15% Free Content Customization
• Detailed Report Walkthrough Session with Research Team
• Free Updated report if the report is 6-12 months old or older

TABLE OF CONTENTS

SECTION I: REPORT OVERVIEW

1. PREFACE

• 1.1. Introduction
• 1.2. Market Share Insights
• 1.3. Key Market Insights
• 1.4. Report Coverage
• 1.5. Key Questions Answered
• 1.6. Chapter Outlines

2. RESEARCH METHODOLOGY

• 2.1. Chapter Overview
• 2.2. Research Assumptions
• 2.3. Database Building
  • 2.3.1. Data Collection
  • 2.3.2. Data Validation
  • 2.3.3. Data Analysis
• 2.4. Project Methodology
  • 2.4.1. Secondary Research
    • 2.4.1.1. Annual Reports
    • 2.4.1.2. Academic Research Papers
    • 2.4.1.3. Company Websites
    • 2.4.1.4. Investor Presentations
    • 2.4.1.5. Regulatory Filings
    • 2.4.1.6. White Papers
    • 2.4.1.7. Industry Publications
    • 2.4.1.8. Conferences and Seminars
    • 2.4.1.9. Government Portals
    • 2.4.1.10. Media and Press Releases
    • 2.4.1.11. Newsletters
    • 2.4.1.12. Industry Databases
    • 2.4.1.13. Roots Proprietary Databases
    • 2.4.1.14. Paid Databases and Sources
    • 2.4.1.15. Social Media Portals
    • 2.4.1.16. Other Secondary Sources
  • 2.4.2. Primary Research
    • 2.4.2.1. Introduction
    • 2.4.2.2. Types
      • 2.4.2.2.1. Qualitative
      • 2.4.2.2.2. Quantitative
    • 2.4.2.3. Advantages
    • 2.4.2.4. Techniques
      • 2.4.2.4.1. Interviews
      • 2.4.2.4.2. Surveys
      • 2.4.2.4.3. Focus Groups
      • 2.4.2.4.4. Observational Research
      • 2.4.2.4.5. Social Media Interactions
    • 2.4.2.5. Stakeholders
      • 2.4.2.5.1. Company Executives (CXOs)
      • 2.4.2.5.2. Board of Directors
      • 2.4.2.5.3. Company Presidents and Vice Presidents
      • 2.4.2.5.4. Key Opinion Leaders
      • 2.4.2.5.5. Research and Development Heads
      • 2.4.2.5.6. Technical Experts
      • 2.4.2.5.7. Subject Matter Experts
      • 2.4.2.5.8. Scientists
      • 2.4.2.5.9. Doctors and Other Healthcare Providers
    • 2.4.2.6. Ethics and Integrity
      • 2.4.2.6.1. Research Ethics
      • 2.4.2.6.2. Data Integrity
  • 2.4.3. Analytical Tools and Databases

3. MARKET DYNAMICS

• 3.1. Forecast Methodology
  • 3.1.1. Top-Down Approach
  • 3.1.2. Bottom-Up Approach
  • 3.1.3. Hybrid Approach
• 3.2. Market Assessment Framework
  • 3.2.1. Total Addressable Market (TAM)
  • 3.2.2. Serviceable Addressable Market (SAM)
  • 3.2.3. Serviceable Obtainable Market (SOM)
  • 3.2.4. Currently Acquired Market (CAM)
• 3.3. Forecasting Tools and Techniques
  • 3.3.1. Qualitative Forecasting
  • 3.3.2. Correlation
  • 3.3.3. Regression
  • 3.3.4. Time Series Analysis
  • 3.3.5. Extrapolation
  • 3.3.6. Convergence
  • 3.3.7. Forecast Error Analysis
  • 3.3.8. Data Visualization
  • 3.3.9. Scenario Planning
  • 3.3.10. Sensitivity Analysis
• 3.4. Key Considerations
  • 3.4.1. Demographics
  • 3.4.2. Market Access
  • 3.4.3. Reimbursement Scenarios
  • 3.4.4. Industry Consolidation
• 3.5. Robust Quality Control
• 3.6. Key Market Segmentations
• 3.7. Limitations

4. MACRO-ECONOMIC INDICATORS

• 4.1. Chapter Overview
• 4.2. Market Dynamics
  • 4.2.1. Time Period
    • 4.2.1.1. Historical Trends
    • 4.2.1.2. Current and Forecasted Estimates
  • 4.2.2. Currency Coverage
    • 4.2.2.1. Overview of Major Currencies Affecting the Market
    • 4.2.2.2. Impact of Currency Fluctuations on the Industry
  • 4.2.3. Foreign Exchange Impact
    • 4.2.3.1. Evaluation of Foreign Exchange Rates and Their Impact on Market
    • 4.2.3.2. Strategies for Mitigating Foreign Exchange Risk
  • 4.2.4. Recession
    • 4.2.4.1. Historical Analysis of Past Recessions and Lessons Learnt
    • 4.2.4.2. Assessment of Current Economic Conditions and Potential Impact on the Market
  • 4.2.5. Inflation
    • 4.2.5.1. Measurement and Analysis of Inflationary Pressures in the Economy
    • 4.2.5.2. Potential Impact of Inflation on the Market Evolution
  • 4.2.6. Interest Rates
    • 4.2.6.1. Overview of Interest Rates and Their Impact on the Market
    • 4.2.6.2. Strategies for Managing Interest Rate Risk
  • 4.2.7. Commodity Flow Analysis
    • 4.2.7.1. Type of Commodity
    • 4.2.7.2. Origins and Destinations
    • 4.2.7.3. Values and Weights
    • 4.2.7.4. Modes of Transportation
  • 4.2.8. Global Trade Dynamics
    • 4.2.8.1. Import Scenario
    • 4.2.8.2. Export Scenario
  • 4.2.9. War Impact Analysis
    • 4.2.9.1. Russian-Ukraine War
    • 4.2.9.2. Israel-Hamas War
  • 4.2.10. COVID Impact / Related Factors
    • 4.2.10.1. Global Economic Impact
    • 4.2.10.2. Industry-specific Impact
    • 4.2.10.3. Government Response and Stimulus Measures
    • 4.2.10.4. Future Outlook and Adaptation Strategies
  • 4.2.11. Other Indicators
    • 4.2.11.1. Fiscal Policy
    • 4.2.11.2. Consumer Spending
    • 4.2.11.3. Gross Domestic Product (GDP)
    • 4.2.11.4. Employment
    • 4.2.11.5. Taxes
    • 4.2.11.6. R&D Innovation
    • 4.2.11.7. Stock Market Performance
    • 4.2.11.8. Supply Chain
    • 4.2.11.9. Cross-Border Dynamics

SECTION II: QUALITATIVE INSIGHTS

5. EXECUTIVE SUMMARY

6. INTRODUCTION

• 6.1. Chapter Overview
• 6.2. Overview of Natural Language Processing Market
  • 6.2.1. Type of Component
  • 6.2.2. Type of Processing
  • 6.2.3. Type of Deployment
  • 6.2.4. Type of Application
  • 6.2.5. Type of End User
• 6.3. Future Perspective

7. REGULATORY SCENARIO

SECTION III: MARKET OVERVIEW

8. COMPREHENSIVE DATABASE OF LEADING PLAYERS

9. COMPETITIVE LANDSCAPE

• 9.1. Chapter Overview
• 9.2. Natural Language Processing: Overall Market Landscape
  • 9.2.1. Analysis by Year of Establishment
  • 9.2.2. Analysis by Company Size
  • 9.2.3. Analysis by Location of Headquarters
  • 9.2.4. Analysis by Ownership Structure

10. WHITE SPACE ANALYSIS

11. COMPANY COMPETITIVENESS ANALYSIS

12. STARTUP ECOSYSTEM IN THE NATURAL LANGUAGE PROCESSING MARKET

• 12.1. Natural Language Processing: Market Landscape of Startups
  • 12.1.1. Analysis by Year of Establishment
  • 12.1.2. Analysis by Company Size
  • 12.1.3. Analysis by Company Size and Year of Establishment
  • 12.1.4. Analysis by Location of Headquarters
  • 12.1.5. Analysis by Company Size and Location of Headquarters
  • 12.1.6. Analysis by Ownership Structure
• 12.2. Key Findings

SECTION IV: COMPANY PROFILES

13. COMPANY PROFILES

• 13.1. Chapter Overview
• 13.2. 3M *
  • 13.2.1. Company Overview
  • 13.2.2. Company Mission
  • 13.2.3. Company Footprint
  • 13.2.4. Management Team
  • 13.2.5. Contact Details
  • 13.2.6. Financial Performance
  • 13.2.7. Operating Business Segments
  • 13.2.8. Service / Product Portfolio (project specific)
  • 13.2.9. MOAT Analysis
  • 13.2.10. Recent Developments and Future Outlook
• 13.3. Amazon
• 13.4. Apple
• 13.5. Baidu
• 13.6. Crayon Data
• 13.7. Google
• 13.8. Hewlett Packard
• 13.9. IBM
• 13.10. Inbenta Holding
• 13.11. IQVIA
• 13.12. Just AI
• 13.13. Linguamatics
• 13.14. Meta Platforms
• 13.15. Microsoft
• 13.16. NetBase Quid
• 13.17. Open AI
• 13.18. Oracle
• 13.19. Rasa
• 13.20. Rasa
• 13.21. SAP
• 13.22. SAS
• 13.23. SoundHound AI

SECTION V: MARKET TRENDS

14. MEGA TRENDS ANALYSIS

15. UNMEET NEED ANALYSIS

16. PATENT ANALYSIS

17. RECENT DEVELOPMENTS

• 17.1. Chapter Overview
• 17.2. Recent Funding
• 17.3. Recent Partnerships
• 17.4. Other Recent Initiatives

SECTION VI: MARKET OPPORTUNITY ANALYSIS

18. GLOBAL NATURAL LANGUAGE PROCESSING MARKET

• 18.1. Chapter Overview
• 18.2. Key Assumptions and Methodology
• 18.3. Trends Disruption Impacting Market
• 18.4. Demand Side Trends
• 18.5. Supply Side Trends
• 18.6. Global Natural Language Processing Market, Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
• 18.7. Multivariate Scenario Analysis
  • 18.7.1. Conservative Scenario
  • 18.7.2. Optimistic Scenario
• 18.8. Investment Feasibility Index
• 18.9. Key Market Segmentations

19. MARKET OPPORTUNITIES BASED ON TYPE OF COMPONENT

• 19.1. Chapter Overview
• 19.2. Key Assumptions and Methodology
• 19.3. Revenue Shift Analysis
• 19.4. Market Movement Analysis
• 19.5. Penetration-Growth (P-G) Matrix
• 19.6. Natural Language Processing Market for Solutions: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
• 19.7. Natural Language Processing Market for Services: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
• 19.8. Data Triangulation and Validation
  • 19.8.1. Secondary Sources
  • 19.8.2. Primary Sources
  • 19.8.3. Statistical Modeling

20. MARKET OPPORTUNITIES BASED ON TYPE OF PROCESSING

• 20.1. Chapter Overview
• 20.2. Key Assumptions and Methodology
• 20.3. Revenue Shift Analysis
• 20.4. Market Movement Analysis
• 20.5. Penetration-Growth (P-G) Matrix
• 20.6. Natural Language Processing Market for Hybrid NLP: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
• 20.7. Natural Language Processing Market for Rule Based NLP: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
• 20.8. Natural Language Processing Market for Statistical NLP: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
• 20.9. Data Triangulation and Validation
  • 20.9.1. Secondary Sources
  • 20.9.2. Primary Sources
  • 20.9.3. Statistical Modeling

21. MARKET OPPORTUNITIES BASED ON TYPE OF DEPLOYMENT

• 21.1. Chapter Overview
• 21.2. Key Assumptions and Methodology
• 21.3. Revenue Shift Analysis
• 21.4. Market Movement Analysis
• 21.5. Penetration-Growth (P-G) Matrix
• 21.6. Natural Language Processing Market for Cloud-Bases: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
• 21.7. Natural Language Processing Market for On-Premises: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
• 21.8. Data Triangulation and Validation
  • 21.8.1. Secondary Sources
  • 21.8.2. Primary Sources
  • 21.8.3. Statistical Modeling

22. MARKET OPPORTUNITIES BASED ON TYPE OF APPLICATION

• 22.1. Chapter Overview
• 22.2. Key Assumptions and Methodology
• 22.3. Revenue Shift Analysis
• 22.4. Market Movement Analysis
• 22.5. Penetration-Growth (P-G) Matrix
• 22.6. Natural Language Processing Market for Customer Experience Management: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
• 22.7. Natural Language Processing Market for Machine Translation: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
• 22.8. Natural Language Processing Market for Sentiment Analysis: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
• 22.9. Natural Language Processing Market for Social Media Monitoring: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
• 22.10. Natural Language Processing Market for Text Classification & Summarization: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
• 22.11. Natural Language Processing Market for Virtual Assistants / Chatbots: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
• 22.12. Natural Language Processing Market for Others: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
• 22.13. Data Triangulation and Validation
  • 22.13.1. Secondary Sources
  • 22.13.2. Primary Sources
  • 22.13.3. Statistical Modeling

23. MARKET OPPORTUNITIES BASED ON TYPE OF END USER

• 23.1. Chapter Overview
• 23.2. Key Assumptions and Methodology
• 23.3. Revenue Shift Analysis
• 23.4. Market Movement Analysis
• 23.5. Penetration-Growth (P-G) Matrix
• 23.6. Natural Language Processing Market for BFSI: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
• 23.7. Natural Language Processing Market for Education: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
• 23.8. Natural Language Processing Market for Healthcare: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
• 23.9. Natural Language Processing Market for IT& Telecom: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
• 23.10. Natural Language Processing Market for Manufacturing: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
• 23.11. Natural Language Processing Market for Media & Entertainment: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
• 23.12. Natural Language Processing Market for Retail & E-Commerce: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
• 23.13. Natural Language Processing Market for Others: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
• 23.14. Data Triangulation and Validation
  • 23.14.1. Secondary Sources
  • 23.14.2. Primary Sources
  • 23.14.3. Statistical Modeling

24. MARKET OPPORTUNITIES FOR NATURAL LANGUAGE PROCESSING IN NORTH AMERICA

• 24.1. Chapter Overview
• 24.2. Key Assumptions and Methodology
• 24.3. Revenue Shift Analysis
• 24.4. Market Movement Analysis
• 24.5. Penetration-Growth (P-G) Matrix
• 24.6. Natural Language Processing Market in North America: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 24.6.1. Natural Language Processing Market in the US: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 24.6.2. Natural Language Processing Market in Canada: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 24.6.3. Natural Language Processing Market in Mexico: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 24.6.4. Natural Language Processing Market in Other North American Countries: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
• 24.7. Data Triangulation and Validation

25. MARKET OPPORTUNITIES FOR NATURAL LANGUAGE PROCESSING IN EUROPE

• 25.1. Chapter Overview
• 25.2. Key Assumptions and Methodology
• 25.3. Revenue Shift Analysis
• 25.4. Market Movement Analysis
• 25.5. Penetration-Growth (P-G) Matrix
• 25.6. Natural Language Processing Market in Europe: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 25.6.1. Natural Language Processing Market in Austria: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 25.6.2. Natural Language Processing Market in Belgium: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 25.6.3. Natural Language Processing Market in Denmark: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 25.6.4. Natural Language Processing Market in France: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 25.6.5. Natural Language Processing Market in Germany: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 25.6.6. Natural Language Processing Market in Ireland: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 25.6.7. Natural Language Processing Market in Italy: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 25.6.8. Natural Language Processing Market in Netherlands: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 25.6.9. Natural Language Processing Market in Norway: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 25.6.10. Natural Language Processing Market in Russia: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 25.6.11. Natural Language Processing Market in Spain: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 25.6.12. Natural Language Processing Market in Sweden: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 25.6.13. Natural Language Processing Market in Switzerland: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 25.6.14. Natural Language Processing Market in the UK: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 25.6.15. Natural Language Processing Market in Other European Countries: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
• 25.7. Data Triangulation and Validation

26. MARKET OPPORTUNITIES FOR NATURAL LANGUAGE PROCESSING IN ASIA

• 26.1. Chapter Overview
• 26.2. Key Assumptions and Methodology
• 26.3. Revenue Shift Analysis
• 26.4. Market Movement Analysis
• 26.5. Penetration-Growth (P-G) Matrix
• 26.6. Natural Language Processing Market in Asia: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 26.6.1. Natural Language Processing Market in China: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 26.6.2. Natural Language Processing Market in India: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 26.6.3. Natural Language Processing Market in Japan: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 26.6.4. Natural Language Processing Market in Singapore: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 26.6.5. Natural Language Processing Market in South Korea: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 26.6.6. Natural Language Processing Market in Other Asian Countries: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
• 26.7. Data Triangulation and Validation

27. MARKET OPPORTUNITIES FOR NATURAL LANGUAGE PROCESSING IN MIDDLE EAST AND NORTH AFRICA (MENA)

• 27.1. Chapter Overview
• 27.2. Key Assumptions and Methodology
• 27.3. Revenue Shift Analysis
• 27.4. Market Movement Analysis
• 27.5. Penetration-Growth (P-G) Matrix
• 27.6. Natural Language Processing Market in Middle East and North Africa (MENA): Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 27.6.1. Natural Language Processing Market in Egypt: Historical Trends (Since 2019) and Forecasted Estimates (Till 205)
  • 27.6.2. Natural Language Processing Market in Iran: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 27.6.3. Natural Language Processing Market in Iraq: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 27.6.4. Natural Language Processing Market in Israel: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 27.6.5. Natural Language Processing Market in Kuwait: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 27.6.6. Natural Language Processing Market in Saudi Arabia: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 27.6.7. Neuromorphic Computing Marke in United Arab Emirates (UAE): Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 27.6.8. Natural Language Processing Market in Other MENA Countries: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
• 27.7. Data Triangulation and Validation

28. MARKET OPPORTUNITIES FOR NATURAL LANGUAGE PROCESSING IN LATIN AMERICA

• 28.1. Chapter Overview
• 28.2. Key Assumptions and Methodology
• 28.3. Revenue Shift Analysis
• 28.4. Market Movement Analysis
• 28.5. Penetration-Growth (P-G) Matrix
• 28.6. Natural Language Processing Market in Latin America: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 28.6.1. Natural Language Processing Market in Argentina: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 28.6.2. Natural Language Processing Market in Brazil: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 28.6.3. Natural Language Processing Market in Chile: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 28.6.4. Natural Language Processing Market in Colombia Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 28.6.5. Natural Language Processing Market in Venezuela: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 28.6.6. Natural Language Processing Market in Other Latin American Countries: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
• 28.7. Data Triangulation and Validation

29. MARKET OPPORTUNITIES FOR NATURAL LANGUAGE PROCESSING IN REST OF THE WORLD

• 29.1. Chapter Overview
• 29.2. Key Assumptions and Methodology
• 29.3. Revenue Shift Analysis
• 29.4. Market Movement Analysis
• 29.5. Penetration-Growth (P-G) Matrix
• 29.6. Natural Language Processing Market in Rest of the World: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 29.6.1. Natural Language Processing Market in Australia: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 29.6.2. Natural Language Processing Market in New Zealand: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 29.6.3. Natural Language Processing Market in Other Countries
• 29.7. Data Triangulation and Validation

30. MARKET CONCENTRATION ANALYSIS: DISTRIBUTION BY LEADING PLAYERS

• 30.1. Leading Player 1
• 30.2. Leading Player 2
• 30.3. Leading Player 3
• 30.4. Leading Player 4
• 30.5. Leading Player 5
• 30.6. Leading Player 6
• 30.7. Leading Player 7
• 30.8. Leading Player 8

31. ADJACENT MARKET ANALYSIS

SECTION VII: STRATEGIC TOOLS

32. KEY WINNING STRATEGIES

33. PORTER'S FIVE FORCES ANALYSIS

34. SWOT ANALYSIS

35. VALUE CHAIN ANALYSIS

36. ROOTS STRATEGIC RECOMMENDATIONS

SECTION VIII: OTHER EXCLUSIVE INSIGHTS

37. INSIGHTS FROM PRIMARY RESEARCH

38. REPORT CONCLUSION

SECTION IX: APPENDIX

39. TABULATED DATA

40. LIST OF COMPANIES AND ORGANIZATIONS

41. CUSTOMIZATION OPPORTUNITIES

42. ROOTS SUBSCRIPTION SERVICES

43. AUTHOR DETAILS

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