Scientific Text Analytics And Annotators Market to Incur Rapid Extension During 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.

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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.

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 tries to recognize nouns which could be a person’s name, country, organization, date, monetary amounts, or the likes of it.

Scientific Text Analytics and Annotators Market: Drivers and Challenges

The scientific text analytics and annotators market is expected to rise in the future as solution providers are merging big data with text analytics which can be used across various sectors such as healthcare, finance, banking, retail, government, and many more. Organizations can benefit by getting a competitive edge over their competitors, or understand their current target market in a better way; for example – through social media tracking, a retail company can find out whether its customers are satisfied with its new product by tracking certain keywords or phrases.

Also, cloud-based text analytics solutions are rapidly growing in the market as companies are themselves going through a transformation (due to digitization). Also, many small and medium scale enterprises find cloud-based solutions to be more economical as they require less storage space and lower maintenance cost.

Another market driver is that text analytics tool can break the language barrier. The emergence of multilingual text analytical tools has helped multi-national companies in gathering data and interpreting information in a more efficient manner. Also, every business has its own set of requirements and applications. There is an increase in industry-specific text analytics tools which has led to many companies adopting this solution.

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One of the challenges faced is sometimes a customer tends to be sarcastic while commenting on social media platform, or during a customer service call, they seem to be agitated (the reason could be altogether different), the text analytics and annotator tool might not be able to capture the true sentiment of the customer. This is due to lack of such level of advancement in the solution which can lead to wrong interpretations.

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This release was published on openPR.