Text analytics, also known as text mining, is the procedure of inspecting large collections of resources for producing new information and convert unstructured texts into structured data for usage in additional analysis. Text analytics recognizes the assertions, relationships and facts that are buried in the mass of documented big data. Facts of text analytics are turned and extracted in the structured data for refinement by using the machine learning, integration with structured data in warehouses or databases and visualization and analysis.

Most developed text analytics software use the sophisticated Natural Language Processing (NLP) algorithms. NLP permits the software to identify similar concepts and express in different ways. Machine learning and NLP are branches of artificial intelligence. Machine learning techniques can be utilized in helping natural language processing tasks. Natural language processing can be used to improve machine learning, mainly by utilizing it to remove the biggest evidence base of structured data for learning the machine learning algorithms.

Analyzing and mining text supports organizations in finding the possibly valuable business insights in the customer emails, corporate documents, social network posts, logs of call center, survey comments of verbatim, medical records and different sources of text-based data. Progressively, capabilities of text mining are being incorporated into virtual agents and artificial intelligence chatbots where companies organize to deliver the automated responses to consumers as the part of marketing, customer service operations and sales.

Text analytics or text mining is the same in nature to data mining with the concentration on text rather than the structured forms of data. On the other hand, the first steps in the process of text analytics are to manage and construct the data in fashion as it can be subjected to quantitative and qualitative analysis. On doing the use of natural language processing technology (NLP) applies the computational morphology principles to interpret and analyze the data sets.

Consequently, tools of text analytics are equipped to expose the fundamental similarities and relations in text data, if the data scientists lack good understanding of the likes finding at the start of the project. For instance, the unofficial model can establish the data from emails or text documents in the group of topics deprived of any guidance from the analyst.

Text Analytics Application

Sentiment analysis is mostly used as the application of text analytics that can track the sentiment of the customer regarding the company. It is also known as opinion mining, social networks, sentiment analysis excavates text from the online reviews, emails, interactions of call centers and the data sources for recognizing the common threads pointing the negative or positive feelings on the basis of consumers. This information can be utilized to solve the issues of product, plan the novel marketing campaigns and enhance the customer service, amongst others.

other common text analytics applications involve blocking spam emails, screening job applicants based on the wording in resumes, categorizing website content, examining the descriptions of medical symptoms to help in diagnoses, flagging the insurance claims that may be fake and studying the corporate documents as the part of electronic discovery processes.

Chatbots answer the questions regarding products and use the basic customer service tasks, by using the Natural Language Understanding technology, the subcategory of NLP helping the bots in understanding the human speech and written text to respond properly.

Natural Language Generation (NLG) is the related technology that excavates images, documents and data and generates text on their own. For instance, NLG algorithms are used in writing the descriptions of vicinities the real estate explanations and listings of the key performance indicators followed by the systems of business intelligence.