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.
Key Market Players
The major players active in the Global Scientific text analytics and annotators market include
- SAS Institute Inc.,
- IBM Corporation,
- SAP SE,
- MeaningCloud LLC,
- Smartlogic,
- Lexalytics,
- Provalis Research,
- OpenText Corporation,
- Pingar,
- AlchemyAPI and
- RapidMiner, Inc..
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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 tries to recognize nouns which could be a person’s name, country, organization, date, monetary amounts, or the likes of it.
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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.
Scientific Text Analytics and Annotators Market: Segmentation
Segmentation On The Basis Of Deployment Type:
- Cloud
- On-Premises
Segmentation On The Basis Of Application:
- Marketing and Customer Experience Management
- Data analysis and forecasting
- Enterprise Information Management
- Other industry specific applications
Segmentation On The Basis Of End-User:
- Banking
- Financial services and Insurance
- healthcare
- retail
- military and defense
- IT and telecom
- entertainment and media
- Others (Transportation, Hospitality, Automotive)
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