In the present digitized world, 80% of the data generated is unstructured. Organizations are using natural language processing technology to unravel the meaning of such data to leverage business strategies and opportunities. A myriad of unstructured data is available online in the form of audio content, visual content and social footprints. Data has now become an asset for organizations. We have arrived into an era of automation of tedious cognitive tasks in businesses. Human beings fundamentally think, communicate and understand in an unstructured manner. Majority of the workflow in business and personal domain are either entirely controlled by humans or involves a human layer that converts the real-world inputs to computer inputs. NLP is gradually becoming ubiquitous in business enterprises and it has a wide array of functions ranging from chatbots and digital assistants such as Google Home, Siri and Alexa to compliance monitoring functions, business intelligence and analytics. Queries, email communication, videos, social media, support requests, customer reviews and so on are sources of useful insights that can be used to generate significant business value.
Natural language processing (NLP), also known as computational linguistics is an amalgamation of artificial intelligence, machine learning and linguistics. NLP is one of the most leveraged technologies in artificial intelligence and the growth of the technology is being propelled by the growth of related technologies such as deep learning and cognitive computing. NLP combines artificial intelligence, computer science and computational linguistics to help machines in reading texts by simulating the human ability of understanding languages. The technology offers competitive advantage to businesses in legal, media and digital ad services. Automotive, healthcare, education and the retail sectors are extensively investing in the technology, as NLP is continuously evolving and is capable of interpreting and adapting to a wide variety of human languages. Sentiment analysis is largely used in web and social media monitoring as it gives businesses access to the opinions of end-users about the organization and its services. Useful insights about customer preferences and attitudes can be obtained from the emoticons in social media. The use cases for natural language processing is diverse, covering customer service, autonomous vehicles, healthcare, banking, financial services and insurance (BFSI), manufacturing, retail and consumer goods, media and entertainment, research, education,high tech and electronics.
Technological mainstays namely Google, IBM, Microsoft and others are making significant investment in the field of natural language processing. NLP and text analytics have a major role to play in social media sentiment analysis, business intelligence, data governance, cognitive computing and business intelligence. Text analytics is a subset of NLP and is one amongst the two analytics options that NLP offers, alongside speech analytics. NLP helps in establishing relationships in documents, carrying out search, understanding the demarcations of sentences and phrases and determining names and places through semantic technologies. In the context of text analytics, NLP helps in identifying aspects of regulatory compliance, categorization, sentiment analysis and text clustering. NLP solutions are either statistics based, rule based or a hybrid.
According to Infoholic Research, the Global Natural Language Processing market is expected to grow at a CAGR of 18.78% during the forecast period 2017–2023. The market is driven by factors such as the availability of a high volume of unstructured data, enhanced utility of smart devices, increased use of NLP in call centers, increased demand for better customer experience and expansive application areas. The future potential of the market is promising owing to opportunities such as developments in big data technologies, democratization of data, smart search and the emergence of human-like virtual assistants. The market growth is curbed by restraining factors such as difficulties in bridging gaps between humans and machines, training of researchers and loss of context and meaning.
Segmentation by Offerings
The market has been segmented and analyzed by the following offerings: Software, Hardware and Services.
Segmentation by Technologies:
The market has been segmented and analyzed by the following technologies: Pattern and Image Recognition, Interactive Voice Response (IVR), Optical Character Recognition (OCR), Text Analytics, Speech Analytics, Classification and Categorization, Auto Coding, Professional Services and Support and Maintenance Services.
Segmentation by Regions:
The market has been segmented and analyzed by the following regions: North America, EMEA, APAC and Latin America.
Segmentation by Verticals:
The market has been segmented and analyzed by the following verticals: Healthcare and Lifesciences, Retail and Consumer Goods, High Tech and Electronics, Media and Entertainment, BFSI, Manufacturing, and Research and Education.
The study covers and analyses the “Global Natural Language Processing Market”. Bringing out the complete key insights of the industry, the report aims to provide an opportunity for players to understand the latest trends, current market scenario, government initiatives, and technologies relevant to the market. In addition, it helps the venture capitalists in understanding the companies better and take informed decisions.
1. The report covers drivers, restraints, and opportunities (DRO) affecting the market growth during the forecast period (2017–2023).
2. It also contains an analysis of vendor profiles, which include financial health, business units, key business priorities, SWOT, strategies, and views.
3. The report covers competitive landscape, which includes M&A, joint ventures and collaborations, and competitor comparison analysis.
4. In the vendor profile section, for the companies that are privately held, financial information and revenue of segments will be limited.