Scientific Text Analytics and Annotators Market Growth, Forecast and Value Chain 2016-2026

Scientific text analytics refers to the process of analyzing unstructured raw data, extracting relevant information from it and transforming it into useful business information. An annotator is something that can supply or furnish critical or explanatory notes or comments. Text analytics and annotators combined together can help companies in fetching relevant information from a large amount of data by capturing certain keywords, phrases, classifications or entities which help them in determining the sentiments of user.

The various steps that are carried out in text analytics is text identification, text mining, text categorization, text clustering, search access, entity/relation modeling, link analysis, sentiment analysis, summarization and visualization.

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There are varied application of text analytics which are listed below:

Sentiment analysis

Search access of unstructured data

Email spam filters

Automated AD placement

Social media monitoring

Competitive intelligence

Enterprise business intelligence and data mining

E-discovery, records management

National security and intelligence

Competitive intelligence

Scientific discovery

Few of them which are commonly used are:

Sentiment Analysis: Suppose there is an online portal for garments. There is an online submission form for customers to send in their feedback regarding their purchase. The owner would like to find out the response (positive and negative) of all its customers. In this case they can make use of text analytics tool to find out the sentiment of the customer; whether they are satisfied or dissatisfied with the product.

Topic modeling: It is a technique for finding out most commonly used (discussed) themes from a vast array of topics. It is mostly used by legal firms for digging out information regarding a high profile case.

Named entity recognition: It basically...