Dublin, Feb. 07, 2024 (GLOBE NEWSWIRE) — The “Global Natural Language Processing (NLP) Market by Offering (Solutions, Services), Type (Rule-based, Statistical, Hybrid), Application (Sentiment Analysis, Social Media Monitoring), Technology (IVR, OCR, Auto Coding), Vertical & Region – Forecast to 2028” report has been added to ResearchAndMarkets.com’s offering.
The global natural language processing market is valued at USD 18.9 billion in 2023 and is estimated to reach USD 68.1 billion by 2028, registering a CAGR of 29.3% during the forecast period.
Market Dynamics
- Drivers
- Advancements in Text-Analyzing Computer Programs
- Rising Need to Streamline Business Operations for Better Customer Experience
- Surging Demand for Cloud-based NLP Solutions to Reduce Overall Costs and Enhance Scalability
- Demand for Predictive Analytics to Reduce Risks and Identify Growth Opportunities
- Restraints
- Lack of Contextual Understanding Leading to Ambiguity
- Complexities due to Code-Mixed Language
- Limitations in Developing NLP Technology Using Neural Networks
- Opportunities
- Generative AI Acting as Catalyst for Transforming NLP Market
- Need for Developing Big Data Technology for Actionable Business Intelligence
- Increasing Demand for Effective Data Management and Advanced Data Analytics in Healthcare Sector
- Challenges
- Lack of Multilingual Proficiency
- Regulatory and Privacy Concerns Over Data Security
- Interoperability and Reliability Issues while Deploying NLP Algorithms
The Solution segment is projected to hold the largest market size during the forecast period
The adoption of AI solutions is increasing worldwide, as people become more aware of their potential benefits. NLP is a branch of AI that deals with unstructured data processing and mining. It is commonly used to analyze text, voice, and video content, enabling AI agents to understand the nuances and contexts of human language. Companies like IBM, Microsoft, and Google offer NLP solutions with innovative features, such as sentiment analysis, text classification, summarization, and speech recognition. Some vendors provide sophisticated NLP software tools or APIs with customized features tailored to individual user demands in the market.
By Type, Hybrid Segment is registered to grow at the highest CAGR during the forecast period
Hybrid NLP is an advanced approach that combines the strengths of rule-based and statistical NLP methods to provide more accurate language understanding and processing. This fusion integrates predefined linguistic rules with statistical models and ML techniques, enabling for better analysis of vast amounts of data. Hybrid NLP provides several advantages, especially when users do not have proper datasets and need to implement the statistical method with the least dataset. For instance, in the grammar correction system, a module identifies multi-word expressions and then uses the rule-based method to identify incorrect patterns and generate correct ones. Hybrid NLP is particularly effective in scenarios where precise rule-based processing is essential but also benefits from the flexibility and adaptability of statistical approaches. Hybrid NLP is widely used in various NLP applications, such as chatbots, virtual assistants, sentiment analysis, and text classification, providing a versatile solution to tackle the diverse challenges of language understanding and generation. It is also used in eCRM, sentiment analysis, machine translation, and report generation.
By solutions, Software tools segment is anticipated to account for the largest market size during the forecast period
Software tools offered in the NLP market in the form of SDKs, APIs, and frameworks that enable users to integrate NLP capabilities with their existing software. The NLP market witnesses significant developments due to flexible software and SDK kits. Such software tools are used with other solutions to carry out different tasks, such as text and speech analytics. NLU-based business applications are also finding the increased use of Interactive Voice Response (IVR) and virtual assistants in telecommunications and other verticals for enabling users to interact with such systems in their natural language without constraining a set of fixed responses. With statistical techniques, vendors integrate self-learning capabilities to ensure systems keep learning based on user commands and act accordingly in the future. Such techniques have also improved accuracy and helped systems deliver relevant responses. For instance, Google offers Cloud Natural Language APIs, which emphasize entity extraction, sentiment analysis, syntax analysis, and categorization applications.
Asia Pacific is projected to witness the highest CAGR during the forecast period
The Asia Pacific Natural Language Processing Market includes countries such as China, Japan, India, ASEAN Countries, and Rest of Asia Pacific, which comprises countries like Sri Lanka, Bangladesh, and Myanmar. The region is expected to experience significant growth in the adoption of NLP software and services due to government initiatives, policies, and investments, along with the commercialization of AI and ML technologies. As the region holds more than 50% of the world’s population, any technological shifts like those being heralded by AI are expected to shape the future of the region. Many Asian countries such as China, India, Japan, and others are leveraging information-intensive AI technologies, with conversational AI being one of the leading technology trends.
Research Coverage
The market study covers natural language processing across segments. It aims at estimating the market size and the growth potential across different segments, such as offering, type, application, technology, vertical, and region. It includes an in-depth competitive analysis of the key players in the market, along with their company profiles, key observations related to product and business offerings, recent developments, and key market strategies.
Major vendors offering natural language processing solutions and services across the globe are IBM (US), Microsoft (US), Google (US), AWS (US), Meta (US), 3M (US), Baidu (China), Apple (US), SAS Institute (US), IQVIA (UK), Oracle (US), Salesforce (US), OpenAI (US), Inbenta (US), LivePerson (US), SoundHound AI (US), MindMeld (US), Veritone (US), Dolbey (US), Automated Insights (US), Bitext (US), Conversica (US), UiPath (US), Addepto (US), RaGaVeRa (India), Observe.ai (US), Eigen (US), Gnani.ai (India), Crayon Data (Singapore), Narrativa (US), deepset (US), Ellipsis Health (US), DheeYantra (US), Verbit.ai (US), Rasa (US), MonkeyLearn (US), TextRazor (England), and Cohere (Canada).
Key Attributes
| Report Attribute | Details |
| No. of Pages | 323 |
| Forecast Period | 2023-2028 |
| Estimated Market Value (USD) in 2023 | $18.9 Billion |
| Forecasted Market Value (USD) by 2028 | $68.1 Billion |
| Compound Annual Growth Rate | 29.3% |
| Regions Covered | Global |
Technology Analysis
- Key Technologies
- AI
- Deep Learning
- Speech Recognition
- Speech-To-Text
- Text-To-Speech
- Complementary Technologies
- Big Data
- Cloud Computing
- IoT
- Adjacent Technologies
- Blockchain
- Cybersecurity
- Graph Databases
Case Study Analysis
- Banking, Financial Services, and Insurance
- Case Study 1: National Bank of Greece Modernized Its Analytics Platform with SAS Viya on Azure
- Case Study 2: HSBC Implemented LivePerson’s Conversational AI Solutions with Intelligent Automation
- Case Study 3: Observe.AI’s Auto QA Revolutionized Compliance and Performance Optimization of Financial Services Company
- Healthcare & Life Sciences
- Case Study 4: TGH Urgent Care Streamlined Operations and Improved Patient Experience with Liveperson’s Voice Bot
- Case Study 5: Atrius Health Created Queries to Extract Clinical Data from Free-Text Fields Using Linguamatics I2E
- Case Study 6: Huntsman Cancer Institute Optimized Research with Linguamatics NLP Platform
- Case Study 7: Humana’s Voice Agent Reduced Costly Pre-Service Calls with Conversational AI
- IT & ITeS
- Case Study 8: Telenor Adapted to Rapidly Changing Customer Expectations Using SAS Viya
- Retail & eCommerce
- Case Study 9: Wehkamp Generated High-Quality Product Descriptions while Maintaining SEO Focus with NLG
- Case Study 10: Bitext’s Multilingual Synthetic Training Data Transformed Customer Service
- Government & Public Sector
- Case Study 11: Azure Maps Weather Forecasted Data and Sas Event Stream Processing Predicted Flooding Time
- Media & Entertainment
- Case Study 12: Sweetwater Enhanced Customer Experience with Voicebase Conversation Intelligence
- Case Study 13: Hearst Newspapers Used Google Cloud Natural Language API for Content Classification
- Travel & Hospitality
- Case Study 14: SAS Gave Foxwoods Resort Casino Holistic View of Resort Operations
Business Models
- NLP as a Service (NLPaaS)
- NLP Software and Tools Licensing
- NLP Content Generation and Publishing
- NLP Analytics and Insights
- NLP Consulting and Custom Solutions
Companies Profiled
- 3M
- Addepto
- Apple
- Automated Insights
- Aws
- Baidu
- Bitext
- Cohere
- Conversica
- Crayon Data
- Deepset
- Dheeyantra
- Dolbey
- Eigen Technologies
- Ellipsis Health
- Gnani.AI
- IBM
- Inbenta
- Iqvia
- Liveperson
- Meta
- Microsoft
- Mindmeld, Inc.
- Monkeylearn
- Narrativa
- Observe.AI
- Openai
- Oracle
- Ragavera
- Rasa
- Salesforce
- Sas Institute
- Soundhound AI
- Textrazor
- Uipath
- Verbit.AI
- Veritone
For more information about this report visit https://www.researchandmarkets.com/r/r6j38i
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- Global Natural Language Processing (NLP) Market
