Natural Language Processing Market Size, Share Global Analysis Report, 2022 – 2028

Industry Insights

[212+ Pages Report] According to Facts and Factors, the global natural language processing market size was worth USD 4,251.50 million in 2021 and is estimated to grow to USD 13,277 million by 2028, with a compound annual growth rate (CAGR) of approximately 20.90% over the forecast period. The report analyzes the natural language processing market drivers, restraints/challenges, and the effect they have on the demands during the projection period. In addition, the report explores emerging opportunities in the natural language processing market.

Market Overview

Loud natural language processing (NLP) component of artificial intelligence (A.I.), enables computers to understand human language, derive meaning, and facilitate communication through the use of conversational intelligence and voice-enabled A.I. Additionally, NLP capabilities like autocomplete and autocorrect evaluate unique language patterns and find the most suitable choices for the user or audience. Natural language processing also helps to organize and organize the processes by automating a large portion of the physical process and offering analytics and business intelligence for growth. Additionally, the market for natural language processing would see a lot of opportunity due to the growth in data and the complexity of huge enterprises.

An interface for technology and people to communicate is provided by natural language processing. It makes it simple for computers to comprehend the input instructions given by people. It is also regarded as an area of artificial intelligence (A.I.) that deals with human languages' comprehension, decoding, and manipulation. The demand for big data technology and the growing popularity of human-to-machine communications are driving a quick uptake of natural language processing. The input data provided in human languages must also be properly understood for machine learning techniques to work. Natural language processing will become more widely used in the future. It also gives a variety of applications, like acknowledgment or content analytics, and useful numeric structure.

In several industry sectors, including healthcare, manufacturing, BFSI, advertising, and automotive, NLP solutions and services are widely used. In the fields of process automation, robotics, and manufacturing. NLP has been used in robots to aid in improving workflow and production efficiency in the manufacturing sector. As a result, it is likely that these solutions will be adopted more frequently in the near future.

COVID-19 Impact:

Although it is anticipated that COVID-19 will have a short-term impact, forecasts and businesses may be significantly impacted for at least 8 to 12 months. The adoption of cloud-based natural language processing solutions for security and data privacy to keep workers on the task at home has become necessary due to the advent of new habits like working from home and social distancing. Businesses are using a number of fresh, cutting-edge digital solutions in response to the global COVID-19 pandemic. NLP solutions are becoming a new point of distinction and separating out from their rivals. Businesses are now able to improve communication with organizational stakeholders thanks to new and improved NLP technologies. Systems for sharing documents are necessary when businesses need to work swiftly with teams.

In order to make quicker business choices, advanced consumer query analysis and NLP technologies can make sure that everyone can collaborate in real-time on the same version of voice/text data while maintaining better security and privacy. During COVID-19, this has become crucial in order to promote teamwork, enhance organizations, and boost production.

Key Insights

• The most common application is machine translation in 2021.
• During the projected period, the BFSI segment will account for the greatest share of the market. As the BFSI sector rapidly moves toward digitization, NLP is a crucial option for banks.
• The largest market size is predicted to be in North America over the projection period.

Growth Drivers

• Growing Healthcare Sector Demand Will Support Growth

Significant data have been produced as a result of healthcare organizations' increasing usage of digital technology. Gaining data insights requires effective data management. In order to quickly examine enormous amounts of data, analytics-driven approaches are becoming more and more popular. The healthcare industry has also been able to embrace cutting-edge technologies to evaluate, search, and interpret huge volumes of patient data due to the enormous amount of patient data and electronic health records. As a result, it is anticipated that the growing demand from the healthcare industry would aid in expanding the worldwide NLP market throughout the forecast year.

Restraints

• Complexities associated with creating NLP solutions while using code-mixed language

In multilingual societies around the world, code-switching, also known as code-mixed (CM) language, is the alternation of languages within a conversation or utterance. Traditionally, informal or casual conversation has been linked to the CM language. There is proof that the CM language has taken over as the standard form of communication in a number of societies, including metropolitan Mexico and India. It has also influenced written text, particularly in social media and computer-mediated communication.

Working with non-canonical multilingual data that combines two or more languages presents challenges for NLP tasks such as normalization, language identification, language modeling, part-of-speech tagging, dependency parsing, machine translation, and automatic voice recognition (ASR). The properties of mixed data have an impact on tasks in a variety of ways, sometimes by redefining them (for instance, in language identification, by moving from document-level to word-level), and other times by generating novel lexical and syntactic structures (for example, mixed words that consist of morphemes from two different languages).

Opportunities

• Increase in investments in the healthcare vertical

Hospitals and other healthcare facilities are increasingly going digital, which is creating a significant amount of data for the business. Healthcare firms are placing a lot of money on this vast amount of relevant data and analytics-driven methodology. Without advanced technologies, these firms are having trouble, as it is difficult to interpret text data. Natural language processing, however, has emerged as a crucial tool for drawing meaningful conclusions from such a vast amount of data. Over the past ten years, the healthcare sector has experienced a considerable increase in the demand for effective data management and cutting-edge data analytics.

Additionally, in the next five years, it is anticipated that there will be a vast array of potential for NLP technologies in the healthcare sector. Personal Health Records (PHRs) are gaining popularity, and fresh efforts have been made to streamline the downloading and sharing of medical records with various healthcare and insurance providers. With the growing popularity of top-notch data management and mobile analytics apps, this industry is anticipated to develop even more. Formerly concealed in written form.

Challenges

• Interoperability and reliability issues while deploying NLP algorithms

Through the use of computer-based software that can be trained, ML is used to develop A.I. Systems can process data using algorithms and find specific features from the dataset with the aid of machine learning (ML). NLP makes it possible for computers to comprehend human language, but it relies on algorithms built for certain jobs. Numerous system connectivity problems exist, including interoperability, accessibility, and non-contextual responses.

In July 2017, researchers at the Facebook A.I. Research (FAIR) lab discovered the chatbots had strayed from the plan and were speaking a language they had invented that was incomprehensible to humans. Additionally, a small number of tasks can only be carried out by humans and not by machines, and the opposite is also true. For instance, the rise of social media and smartphones has given rise to a new level of derivative languages that can be employed to either jargonize or condense sentences.

Segmentation Analysis

The product type, deployment style, technology, application, and end-user are used to segment the natural language processing market

The various application categories include information extraction, machine translation, processing, and visualization. The most common use is machine translation. The main purpose of NLPs is to translate human language into computer language. The majority of the data entered into the system is in natural languages, so it must be translated into machine language before processing. The market for natural language processing is broken down into a number of verticals, including BFSI, I.T. and telecom, retail and e-commerce, healthcare and life sciences, transportation and logistics, government and public sector, energy and utilities, manufacturing, and others (education, travel and hospitality, and media and entertainment).

During the anticipated period, the BFSI segment will account for the biggest market size. As the BFSI sector rapidly moves toward digitization, NLP is a crucial option for banks. Banks are currently looking to automate compliance procedures, which is possible with the aid of document or information search technologies. Bank compliance officers may be able to identify pertinent information more quickly among thousands of digital documents with the aid of search tools. They can ascertain whether wealth managers are dealing with clients in accordance with laws by locating client data and demonstrating that it has been deleted when clients request that their data be erased in accordance with GDPR.

Report Scope

Recent Developments

• IBM released new natural language processing (NLP) improvements in November 2021. They were developed for IBM Watson Discovery and were meant to assist business users in industries including financial services, insurance, and legal services in enhancing customer service and accelerating business procedures by extracting information and synthesizing data from complicated documents.
• The Verint Intelligent Virtual Assistant (IVA) was recognized in May 2021 as one of the winners in the area of natural language processing (NLP) products at the Business Intelligence Group's Excellence in Artificial Intelligence Awards.
• In February 2020, Kore.Ai announced the creation of an enterprise-grade V.A. platform. The platform enables developers to create virtual assistants by utilizing conversational A.I. capabilities, including NLU, Deep Learning, NLP, and Predictive Analytics.

Regional Landscape

• Advancements in technology are likely to help North America dominate the global market

The largest market size is predicted to be in North America throughout the projection period. North America, one of the most developed areas, has invested a lot in technologies like analytics, A.I., and machine learning. The NLP market has the best potential for income generation in the U.S., which holds the largest market share globally. The rapid infrastructural expansions and the rising use of digital technology are the two primary factors boosting the growth of the NLP market in the area. Due to the increased innovations being investigated by foreign companies with offices in the United States, this market is expanding, and the pace of product introductions in the area is increasing. Digital advancements in the healthcare and life science industries as well as the region's increased use of patient health record systems, are to blame for the quickly growing trend in the area.

Competitive Landscape

Key players within the global Natural Language Processing market include 

• 3M Company 
• Apple Inc. 
• Amazon Web Services 
• Baidu Inc. 
• Convergys Corporation 
• Dolbey Systems Inc. 
• Fuji Xerox 
• Google Inc. 
• H.P. Enterprise 
• IBM Corporation

The Global Natural Language Processing Market is segmented as follows:

By Product

• Rule Based
• Statistical
• Hybrid

By Deployment Model

• Public
• Private
• Hybrid

By Technology

• Recognition
• Analytics
• Operational

By Application

• Information Extraction
• Machine Translation
• Processing
• Visualization

By End-User

• BFSI
• IT and Telecommunication
• Defense
• Government Organization
• Retail and E-Commerce
• Healthcare
• Energy
• Utility

By Region

• North America
  • The U.S.
  • Canada
  • Mexico
• Europe
  • France
  • The UK
  • Spain
  • Germany
  • Italy
  • Nordic Countries
    • Denmark
    • Sweden
    • Norway
  • Benelux Union
    • Belgium
    • The Netherlands
    • Luxembourg
  • Rest of Europe
• Asia Pacific
  • China
  • Japan
  • India
  • Australia
  • South Korea
  • Southeast Asia
    • Indonesia
    • Thailand
    • Malaysia
    • Singapore
    • Rest of Southeast Asia
  • Rest of Asia Pacific
• The Middle East & Africa
  • Saudi Arabia
  • UAE
  • Egypt
  • South Africa
  • Rest of the Middle East & Africa
• Latin America
  • Brazil
  • Argentina
  • Rest of Latin America