Future of Natural Language Processing Market Demand, Size, Share and Rapid Growth, Opportunities by 2028

This research report categorizes the natural language processing market based on Offering, Application, End User, and Region.


Northbrook, IL 60062 -- (SBWIRE) -- 09/28/2023 -- The global Natural Language Processing Market size is estimated to grow from USD 18.9 billion in 2023 to USD 68.1 billion by 2028, at a CAGR of 29.3% during the forecast period, according to research report by MarketsandMarkets™.

Natural Language Processing (NLP) refers to the branch of computer science, specifically the branch of Artificial Intelligence (AI), concerned with giving computers the ability to understand text and spoken words in much the same way human beings can. NLP drives computer programs that translate text from one language to another, respond to spoken commands, and quickly summarize large volumes of text—even in real time.

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Services segment to account for higher CAGR during the forecast period

NLP market relies heavily on its services segment to achieve effective software operations. To increase the efficiency of the entire process, managed and professional services are installed, which are the services considered in this report. Companies such as Microsoft, IBM, and SAS Institute have started providing platforms for embedding NLP technologies. These platforms can be coded across various programming languages. Major players like Microsoft have formed partnerships with SMBs that develop speech-to-text software, making them available across their integrated platforms. For example, AWS offers an Amazon Comprehend service that uses machine learning for extracting key phrases and identifying the language in each text. Amazon Comprehend works seamlessly with any AWS-supported application and offers useful features such as sentiment analysis, tokenization, and automated text file organization.

Cloud segment is expected to hold the largest market size for the year 2023

Organizations can reap numerous benefits by deploying their systems on the cloud. These benefits include easy availability, scalability, reduced operational costs, and hassle-free deployments. AI platform providers are focusing on developing robust cloud-based deployment solutions for their clients, as many organizations have migrated to either private or public cloud. This mode of deployment offers additional flexibility for business operations and real-time deployment ease to companies implementing real-time analytics. The cloud-based deployment of NLP has made it easy for users to apply predictive capabilities to the entire organization. The major vendors offering cloud-based NLP solutions are IBM, Microsoft, AWS, and Google.

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The healthcare and life sciences vertical is projected to grow at the highest CAGR during the forecast period

In the healthcare industry, accuracy and efficiency are crucial because they directly impact human health. Therefore, the margin of error must be close to zero. Natural Language Processing (NLP) offers several applications and use cases that address healthcare challenges. NLP technologies are revolutionizing the healthcare sector by automating the tedious task of transcribing notes from clinical staff, extracting essential information, and enabling clinicians to refine their problem list. With the help of NLP tools, clinicians can quickly filter relevant clinical data from unstructured patient-related documentation, flag any necessary updates, and cross-reference the same with the current problem list. The adoption of Electronic Health Records (EHRs) has increased the demand for NLP solutions in the healthcare sector. NLP solutions are readily implemented in EHRs to convert free text conversations into insights, thereby bridging the gap between complex medical terms and patients' understanding of their health. This helps improve patient interactions, and, in turn, the quality of patient care. NLP solutions also aid in identifying gaps in physicians' performance and potential errors in care delivery, thereby contributing to value-based reimbursements.

The report profiles key players such as IBM (US), Microsoft (US), Google (US), AWS (US), Meta (US) and 3M (US).

Key Dynamic Factors For Natural Language Processing Market:

Technological Innovations: Deep learning, neural networks, and transformer models like BERT and GPT (Generative Pre-trained Transformer) are just a few of the technological innovations that NLP primarily depends on. The market is growing as a result of ongoing improvements to NLP models and algorithms that improve language comprehension, semantic analysis, and sentiment analysis.

Data processing and big data:

The proliferation of digital data and the development of big data technologies have made it possible to train and enhance NLP models with a wealth of data. excellent NLP applications depend on excellent data management and processing technologies.

Increasing Use of AI in Different Industries:

The demand for NLP solutions to extract insights and automate processes from text has considerably increased as a result of the wider adoption of AI and machine learning across numerous industries, including healthcare, finance, retail, customer service, and more.

Sentiment analysis is in greater demand:

Businesses are becoming more and more interested in knowing what customers think and feel in order to make wise judgements. In order to evaluate public mood and adjust their tactics as necessary, businesses must use NLP, which is essential to sentiment analysis.

Virtual Assistants (VAs) and chatbots:

Virtual assistants, chatbots, and conversational AI are becoming more widely used and developed. These technologies' fundamental component, NLP, enables seamless and intuitive interactions between machines and people.

Capabilities for multilingual NLP:

The demand for NLP systems that can effectively process and interpret numerous languages and dialects has increased as a result of globalisation and the requirement for multilingual communication.

Regulatory Compliance and Ethical Considerations: As NLP is used more frequently in delicate industries like banking and healthcare, it is crucial to ensure regulatory compliance and handle ethical issues like prejudice and fairness.

Partnerships and Collaborations: It is now usual for NLP solution providers, technology companies, research institutions, and industry participants to collaborate and partner in order to pool knowledge and resources in order to enhance NLP capabilities and efficiently meet market demands.

User Experience and Human-Centric NLP: The market has been driven by a focus on enhancing user experience through intuitive and human-centric NLP solutions. Widespread adoption of NLP solutions depends on them being made user-friendly and offering value to end users.

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Competitive and Segmentation Analysis:

The market for natural language processing (NLP) is quite competitive and is always changing. Through technology developments, clever alliances, and cutting-edge product offers, a number of significant firms are attempting to obtain a competitive edge. Major corporations are making considerable investments in NLP research and development, resulting in the release of cutting-edge NLP tools and solutions. Examples of these corporations are Google LLC, IBM Corporation, Amazon Web Services, Microsoft Corporation, and Facebook, Inc. Startups and smaller businesses are also building a name for themselves in the NLP scene by specialising in specialised fields that promote innovation and healthy competition.

The NLP market can be broadly categorised in terms of segmentation based on components, deployment models, applications, end-users, and regions. The market is divided into software (including multiple NLP libraries, frameworks, and platforms) and services (such professional and managed services), according to its constituent parts. On-premises and cloud-based deployment are both available as deployment strategies, with the latter increasing popularity due to its scalability, cost-effectiveness, and ease of integration.

Machine translation, sentiment analysis, speech recognition, text categorization, information extraction, and question-answering systems are just a few of the many applications of NLP. Major end-users of NLP include the healthcare, retail, banking, IT and telecommunications, and government sectors. These sectors utilise NLP to improve consumer experiences, optimise business processes, and extract insightful information from massive amounts of unstructured data.

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