Text-based NLP - Worldwide

Advancements in machine learning and deep learning techniques have contributed to the development of text-based natural language processing (NLP). Several trends are shaping the market. For example, the ascent of transformer models such as BERT and GPT have revolutionized the NLP tasks of language understanding, sentiment analysis, and text generation. There is also a growing emphasis on multilingual NLP with the development of cross-lingual models that can transfer knowledge between different languages. Furthermore, ethical considerations such as bias mitigation and fairness in NLP algorithms have gained attention, therefore leading to efforts in developing more inclusive and unbiased text-based NLP models and frameworks.

Several factors will contribute to future growth in text-based natural language processing (NLP). First, the increasing volume of unstructured data that companies are dealing with, such as emails, social media posts, and customer feedback. Second, the increasing adoption of text-based NLP in various industries such as healthcare, finance, and legal, where it is being used for applications such as electronic health record analysis, fraud detection, and contract analysis.

The Text-Based Natural Language Processing (NLP) market is expected to continue growing at a rapid pace over the next decade. The market size is expected to grow at a CAGR of 13.8% from 2023 to 2030, where the Asia-Pacific region is expected to see the highest growth rate in the market due to the increasing adoption of NLP in countries such as China and India.

Data coverage: The data encompasses B2B, B2G, and B2C enterprises. Figures are based on the funding values from different industries for the market.

Modeling approach / Market size:Market sizes are determined through a top-down approach with a bottom-up validation, building on a specific rationale for each market. As a basis for evaluating markets, we use annual financial reports, funding data, and third-party data. In addition, we use relevant key market indicators and data from country-specific associations such as GDP, number of internet users, number of secure internet servers, and internet penetration. This data helps us estimate the market size for each country individually.

Forecasts:In our forecasts, we apply diverse forecasting techniques. The selection of forecasting techniques is based on the behavior of the relevant market. For example, the S-curve function and exponential trend smoothing are well suited to forecast digital products and services due to the non-linear growth of technology adoption. The main drivers are the level of digitalization, the number of secure internet servers, and the revenue of the Public Cloud market.

Additional Notes: The data is modeled using current exchange rates. The impact of the COVID-19 pandemic and the Russian-Ukraine war are considered at a country-specific level. The market is updated twice a year. In some cases, the market is updated on an ad-hoc basis (e.g., when new, relevant data has been released or significant changes within the market have an impact on the projected development). Data from the Statista Consumer Insights Global survey is weighted for representativeness.