Market Analysis and Insights

In 2022, the market for natural language processing (NLP) was estimated to be worth USD 16.5 billion. By 2032, it is expected to have grown to USD 239.11 billion, with a CAGR of 31.5%.

Factors that fuel market expansion include the growing use of smart devices to support smart environments. In addition, there is a growing need for sophisticated text analytics, and internet and connected device usage is rising, all of which are contributing to the market growth for natural language processing. NLP-based apps are also being more widely used across sectors to improve customer experience.

Natural Language Processing (NLP) Market  Scope :

Market Definition

The goal of natural language processing (NLP), a branch of computational linguistics and artificial intelligence (AI), is to make it possible for computers to meaningfully comprehend, interpret, and produce human language. 

By bridging the gap between human communication and computer comprehension, NLP enables machines to interact with text and audio data in the same way that people do. NLP may be used to create text that reads like human speech, which includes chatbot answers, text summarization, and language translation. Text summarization reduces a larger text to a shorter form while retaining its essential concepts, text translation entails translating text from one language to another, and chatbots employ natural language processing (NLP) to produce replies that sound human during discussions.

Key Market Segmentation

Insights on Component

The Solution Segment is Valued for the Highest Share

In terms of components, the solution segment commanded the natural language processing (NLP) market as a whole in 2021, and this trend is anticipated to hold throughout the forecast period. Increasing usage of NLP is a key driver of the market's expansion due to factors including rising company competition, laws, user demands, and project risks. Additionally, NLP solutions provide the capabilities required to analyze both language and numerical data, which is advantageous for the industry. 

Insights on Deployment Mode

The On-Premises Segment Valued for the Highest Share

The on-premise segment had the largest share of the market in 2021, and it is projected that this trend will hold throughout the forecast period. Strong authentication security protections are provided by the on-premise NLP deployment, together with total control and visibility over data. Additionally, this segment's growth is fueled by its simplicity in scaling up to meet company demand and boost efficiency with built-in redundancy.

Insights on Region

The Asia Pacific Region Accounted for the Highest Share

Asia-Pacific is predicted to have the largest increase in natural language processing market trends throughout the course of the projection. The region's total economic expansion, which is propelling it from the developing to the developed phase owing to swift technical improvements, economies' digitalization, and government efforts, is mostly driven by increased disposable income among the region's middle-income people.

Key Company Profiles

The key players operating in the natural language processing market include Apple Inc., IBM Corporation, SAS Institute Inc., Microsoft Corporation, Hewlett-Packard Enterprise, Verint Systems, Inc., Narrative Science, Facebook, Google Inc., and Intel Corporation.

COVID-19 Impact and Market Status

Digital communication channels have increased as a result of lockdowns and social isolation policies. This has boosted the need for NLP technology to automate procedures, improve customer service through chatbots, and analyze client sentiment in real time. 

The increase in remote work has brought attention to the significance of powerful virtual collaboration technologies. The improvement of virtual meetings, transcription services, and voice assistants thanks to NLP has increased the effectiveness of distance communication and cooperation. The significance of reviewing medical literature and research has been highlighted by the epidemic. Large volumes of medical literature have been combed through using NLP algorithms to extract pertinent information, speed up the discovery of new drugs, and enhance medical decision-making.

The fast diffusion of knowledge has made accurate and trustworthy communication essential. To identify and stop the spread of false information and fake news about COVID-19, NLP methods have been employed. NLP technology has been used to track and examine supply chain disturbances brought on by the epidemic. To forecast future supply chain challenges, this involves analyzing news articles, social media, and other text-based data.
Throughout the epidemic, the financial markets have been very erratic. NLP has been used to analyze market sentiment, forecast trends, and determine how the pandemic will affect different industries.

Latest Trends

1. NLP has undergone a revolution thanks to the advent of the Transformer architecture, which drives models like BERT, GPT-3, and others. These models have demonstrated amazing performance on a wide range of NLP tasks after being trained on enormous volumes of text data, facilitating transfer learning and lowering the requirement for task-specific training. 

2. Models like GPT-3 have shown they can complete jobs with little to no examples or even without any examples at all. This skill creates opportunities for NLP applications that are more flexible and all-encompassing. It has become more popular to combine words with various modalities including pictures, movies, and audio. 

3. Applications like picture captioning and video summarization with natural language descriptions are made possible by the ability of multimodal models to comprehend and produce content that incorporates various types of input.

4. With an emphasis on more interesting and contextually aware interactions, conversation systems and chatbot development is still progressing. Customer service, virtual assistants, and many more applications employ conversational AI. In the fields of clinical documentation, image analysis, and drug development, NLP is playing a significant role. Patient care and research are being improved by gleaning insightful information from medical texts and records.

5. NLP models are getting better at interpreting complex feelings and attitudes in text. This has uses in social media analysis, marketing, and client feedback.

Significant Growth Factors

The use of NLP technology has been prompted by the need to automate numerous commercial operations, such as customer service, data analysis, and content creation. 

NLP makes it possible to automate processes involving natural language exchanges more effectively and precisely. The need for NLP has grown quickly as a result of chatbots, virtual assistants, and other conversational AI applications becoming more and more common. These programmes promote user experience and consumer engagement. 

There is a growing need for NLP tools that can analyse and extract insights from these enormous volumes of unstructured textual data as a result of the exponential development of textual data coming from sources like as social media, consumer reviews, and online content. The entrance requirements for using NLP in many applications have been decreased by the emergence of potent pretrained models like BERT and GPT-3.

The ability of NLP to extract insights from patient data, scientific literature, and medical records has expanded usage in the healthcare and life sciences industries, which has fuelled market expansion. Machine translation powered by NLP has advanced as a result of the need for accurate and context-relevant language translation services.

Restraining Factors 

The excellence and variety of training data are critical components of NLP models. Unrepresentative or biased data might amplify biases already present in the data and produce biased outcomes.

NLP models' decision-making processes can be challenging to understand as they get more complicated, raising questions about accountability and transparency.

Concerns concerning data privacy and security are raised by the frequent handling of sensitive and private information by NLP, particularly in applications like chatbots and voice assistants.

NLP models may find it difficult to comprehend jargon, slang, or complex settings with accuracy, which can affect how well they function in some applications.

Environmental issues are brought up by the significant computational and energy requirements for training large-scale NLP models.

Recent Developments in the Global Natural Language Processing (NLP) Market: A Snapshot

• A top conversational AI solution vendor, Kore Ai, unveiled an enterprise-grade VA platform in February of 2020. The freshly available platforms allow programmers the chance to construct a virtual assistant utilizing conversational methods.

• Language interpretability tool (LIT), a tool that can analyze, comprehend, visualize, and carry out audits, will be introduced in August 2020. This is based on the browser, which offers quick access to the data and quick fault detection. 

Key Segments Natural Language Processing (NLP) Market

by Component 
• Solution, 
• Services

by Deployment Mode Overview 
• On Premise
• Cloud

by Type Overview 
• Rule Based NLP, 
• Statistical NLP, 
• Hybrid NLP

by Industry Vertical Overview 
• IT and Telecommunication
• Retail and Ecommerce
• BFSI, 
• Healthcare, 
• Education, 
• Media and Entertainment, 
• Others

Regional Overview 

North America
• U.S
• Canada
Europe
• Germany
• France
• UK
• Rest of Europe
Asia Pacific
• China
• India
• Japan
• Rest of Asia Pacific
South America
• Mexico
• Brazil
• Rest of South America