The report provides an overview of the global market for text analytics and analyzes market trends. Using 2019 as the base year, the report provides estimated market data for the forecast period 2020 through 2025. Revenue forecasts for this period are segmented based on deployment, application, end-user, and geography. Market values have been estimated based on the total revenues of software and services.
The report also focuses on the major driving trends and challenges that will affect the market and the vendor landscape. The report explains the competitive landscape and current trends in the text analytics market. The report concludes with an analysis of the text analytics vendor landscape and includes detailed profiles of the major players in the global text analytics market.
An overview of the global market for text analytics within the industry
Analyses of the global market trends, with data from 2019-2020, estimates for 2021 and 2023, and projections of compound annual growth rates (CAGRs) through 2025
Assessment of the value chain of the text analytics industry ecosystem
Highlights of the impact of the COVID-19 pandemic on the text analytics industry
Estimation of market size and revenue forecasts for the text analytics industry, and corresponding market share analysis by deployment type, application, end-user, and geographical region
Understanding of upcoming market opportunities and areas of focus to forecasting the market into various segments
Identification of major stakeholders in the text analytics market and analysis of competitive landscape for the market leaders
Insight into market dynamics featuring drivers and restraints, and other macroeconomic factors affecting the text analytics industry
Detailed profiles of the major players including Alphabet Inc., Amazon.com, Basis Technology, IBM, Microsoft, SAS, and Texifter LLC
Text analytics is a subset of natural language processing (NLP). Both terms are often used interchangeably by industry vendors. Text analytics falls under the artificial intelligence (AI) family. Technology advances within AI have enabled more accurate and contextual analysis of highly dimensional, multilingual textual data. Text analytics is the process of deriving business insight from textual sources. Text analytics scenarios cover not only a wide set of functional requirements, such as enterprise content management and advanced search engines, but also functions such as social media analytics, email/document analysis and free-form analysis. Simply put, text analytics is the practice of using technology to gather, store and mine textual information to translate large volumes of unstructured text into quantitative data in order to uncover insights, trends and patterns that can be used to inform smarter business decisions.
The COVID-19 pandemic has further propelled the market for text analytics, especially in industries such as life sciences and healthcare, where there is a need to analyze research documents from all over the world and across different languages to come up with new drugs or courses of treatment. At the same time, governments and public health departments are trying to leverage data from social media and other sources to monitor citizen response to COVID measures such as social distancing. Many other industries, such as banking and financial services (that need to process large numbers of documents) are focusing on handling customer complaints and keeping the lights on for their back-office applications as they struggle with reduced staff (as most business process outsourcing (BPO) operations have been affected). These and other organizations are trying to leverage natural language technologies (NLTs), from text analytics to chatbots to document processing, to optimize their operations and move towards digital transformation.
Key Topics Covered:
Chapter 1 Introduction
Study Goals and Objectives
Reasons for Doing This Study
Scope of Report
Chapter 2 Summary and Highlights
Chapter 3 Market and Technology Background
Text Analytics Process
Text Analytics Processing Technologies
Types of Text Analytics
Rise in Adoption of Predictive Analytics and Sentiment Analytics by Various Industries
Continuous Improvements in the Language Processing Algorithms
Increasing Attractiveness of Social Media Analytics
Dearth of Technical Expertise and Technological Awareness