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Text Analytics is the process of converting unstructured text data into meaningful data for analysis, to measure customer opinions, product reviews, feedback, to provide search facility, sentimental analysis and entity modeling to support fact based decision making.
Text analysis uses many linguistic, statistical, and machine learning techniques. Text Analytics involves information retrieval from unstructured data and the process of structuring the input text to derive patters and trends and evaluating and interpreting the output data. It also involves lexical analysis, categorization, clustering, pattern recognition, tagging, annotation, information extraction, link and association analysis, visualization, and predictive analytics. Text Analytics determines key words, topics, category, semantics, tags from the millions of text data available in an organization in different files and formats. The term Text Analytics is roughly synonymous with text mining.
Text analytics software solutions provide tools, servers, analytic algorithm based applications, data mining and extraction tools for converting unstructured data in to meaningful data for analysis. The outputs, which are extracted entities, facts, relationships are generally stored in a relational, XML, and other data warehousing applications for analysis by other tools such as business intelligence tools or big data analytics or predictive analytics tools.
Text Analytics Process Flow
Process and Features of Text Analytics Software
1. Text mining, Text parsing, Text Identification, Text extraction, Text categorization, Text clustering. 2. Extraction of concepts, entities, relations, events. 3. Creation of taxonomies. 4. Search Access, Web crawling, indexing, duplicate document identification. 5. Analyze all major file formats and all major languages- Natural Language/Semantic Toolkits. 6. Entity relation modeling. 7. Link analysis, link text repositories. 8. Ability to identify and analyze sentiments, people, places and other information from websites, internal files, reports, surveys, forms, employee surveys, claims, underwriting notes, medical records, emails, news, blogs, social media, customer surveys, market surveys, online forums, online reviews, review sites, scientific journals, website feedback, call center logs, transcripts, snail mail, sales notes. 9. Document summarization features and records management. 10. Interactive visualization.
Applications of Text Analytics
1. Sentiment Analysis 2. Search access of unstructured data 3. Email spam filters to determine the characteristics of messages to filter that are likely to be advertisements or promotional, phishing or unwanted material 4. Automated ad placement 5. Social media monitoring 6. Competitive intelligence 7. Enterprise business intelligence and data mining 8. E-Discovery, records management 9. National security and intelligence 10. Scientific discovery, especially life sciences 11. Competitive intelligence
Big data, Text Analytics and Predictive Analytics
The big data analytics, data mining and text analytics along with statistics, delivers the capabilities to business users to create predictive intelligence by uncovering patterns and relationships in both the structured and unstructured data. For more information on big data analytics and predictive analytics processing, please review the following article:
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