Scientific Text Analytics And Annotators Market
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Global <strong>Scientific</strong> <strong>Text</strong><br />
<strong>Analytics</strong> <strong>And</strong> <strong>Annotators</strong><br />
<strong>Market</strong><br />
Share, Global Trends,<br />
Analysis, Research, Report,<br />
Opportunities,<br />
Segmentation and Forecast,<br />
2016-2026<br />
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Report Description<br />
<strong>Scientific</strong> text analytics refers to the process of analyzing unstructured raw data,<br />
extracting relevant information from it and transforming it into useful business<br />
information. An annotator is something that can supply or furnish critical or explanatory<br />
notes or comments. <strong>Text</strong> analytics and annotators combined together can help<br />
companies in fetching relevant information from a large amount of data by capturing<br />
certain keywords, phrases, classifications or entities which help them in determining the<br />
sentiments of user.<br />
The various steps that are carried out in text analytics is text identification, text mining,<br />
text categorization, text clustering, search access, entity/relation modeling, link analysis,<br />
sentiment analysis, summarization and visualization.<br />
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There are varied application of text analytics which are listed below:<br />
• Sentiment analysis<br />
• Search access of unstructured data<br />
• Email spam filters<br />
• Automated AD placement
Report Description<br />
• Social media monitoring<br />
• Competitive intelligence<br />
• Enterprise business intelligence and data mining<br />
• E-discovery, records management<br />
• National security and intelligence<br />
• Competitive intelligence<br />
• <strong>Scientific</strong> discovery<br />
Few of them which are commonly used are:<br />
Sentiment Analysis: Suppose there is an online portal for garments. There is an online<br />
submission form for customers to send in their feedback regarding their purchase. The<br />
owner would like to find out the response (positive and negative) of all its customers. In<br />
this case they can make use of text analytics tool to find out the sentiment of the<br />
customer; whether they are satisfied or dissatisfied with the product.<br />
Topic modeling: It is a technique for finding out most commonly used (discussed) themes<br />
from a vast array of topics. It is mostly used by legal firms for digging out information<br />
regarding a high profile case.<br />
Named entity recognition: It basically tries to recognize nouns which could be a person’s<br />
name, country, organization, date, monetary amounts, or the likes of it.
Report Description<br />
<strong>Scientific</strong> <strong>Text</strong> <strong>Analytics</strong> and <strong>Annotators</strong> <strong>Market</strong>: Drivers and Challenges<br />
The scientific text analytics and annotators market is expected to rise in the future as<br />
solution providers are merging big data with text analytics which can be used across<br />
various sectors such as healthcare, finance, banking, retail, government, and many more.<br />
Organizations can benefit by getting a competitive edge over their competitors, or<br />
understand their current target market in a better way; for example – through social<br />
media tracking, a retail company can find out whether its customers are satisfied with its<br />
new product by tracking certain keywords or phrases.<br />
Also, cloud-based text analytics solutions are rapidly growing in the market as companies<br />
are themselves going through a transformation (due to digitization). Also, many small and<br />
medium scale enterprises find cloud-based solutions to be more economical as they<br />
require less storage space and lower maintenance cost.<br />
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Another market driver is that text analytics tool can break the language barrier. The<br />
emergence of multilingual text analytical tools has helped multi-national companies in<br />
gathering data and interpreting information in a more efficient manner. Also, every<br />
business has its own set of requirements and applications. There is an increase in<br />
industry-specific text analytics tools which has led to many companies adopting this<br />
solution.
Report Description<br />
One of the challenges faced is sometimes a customer tends to be sarcastic while<br />
commenting on social media platform, or during a customer service call, they seem to be<br />
agitated (the reason could be altogether different), the text analytics and annotator tool<br />
might not be able to capture the true sentiment of the customer. This is due to lack of<br />
such level of advancement in the solution which can lead to wrong interpretations.<br />
<strong>Scientific</strong> <strong>Text</strong> <strong>Analytics</strong> and <strong>Annotators</strong> <strong>Market</strong>: Segmentation<br />
Segmentation On The Basis Of Deployment Type:<br />
• Cloud<br />
• On-Premises<br />
Segmentation On The Basis Of Application:<br />
• <strong>Market</strong>ing and Customer Experience Management<br />
• Data analysis and forecasting<br />
• Enterprise Information Management<br />
• Other industry specific applications<br />
Segmentation On The Basis Of End-User:<br />
Banking, Financial services and Insurance, healthcare, retail, military and defense, IT and
Report Description<br />
telecom, entertainment and media and Others (Transportation, Hospitality, Automotive).<br />
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Key <strong>Market</strong> Players<br />
The major players active in the Global <strong>Scientific</strong> text analytics and annotators market<br />
include SAS Institute Inc., IBM Corporation, SAP SE, MeaningCloud LLC, Smartlogic,<br />
Lexalytics, Provalis Research, Open<strong>Text</strong> Corporation, Pingar, AlchemyAPI and<br />
RapidMiner, Inc..
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