COVID-19 Impact Review: What to Expect from Natural Language Processing (NLP) Market in 2020? | Know the Challenges and Trend Analysis

P&S Intelligence published a new research report, titled, "Natural Language Processing (NLP) Market Research Report: Industry Trends And Growth Forecast till 2024, By leveraging automatic translation, speech processing, natural language generation (NLG), and sentiment analysis, NLP helps prioritize the extracted information, thereby helping organizations in making the best use of structured as well as unstructured data. Therefore, the growing volume of data, especially in the unstructured form, is driving the natural language processing market, by creating the need for a technology which can help companies in effectively processing and studying it, in order to derive actionable insights.

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Information extraction, machine translation, question answering, text processing, and report generation are the various divisions under the application segment. Among these, the machine translation division dominated the natural language processing market in 2018, as organizations generate a heavy demand for solutions that can translate a given text into numerous languages.

This is itself a result of the rising adoption of chatbots for enhancing the experience of customers, to build brand loyalty and increase revenue.Among the various virtual assistants employed by business in recent years, chatbots have been one of the most popular, as they help companies handle customers' concerns quickly and efficiently.

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Further, chatbots which work on NLP are able to process various languages, which helps in the smoothening of processes, particularly those related to customer support. These chatbots establish the primary contact between the business and customers, sorting out as much of the latter's concerns as possible, before handing over the case to human employees. Across the world, North America was the largest natural language processing market in 2018, owing to the increasing need for better customer experience, rising volume of data, and heavy investments in research and development (R&D) in the field of AI.

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