Text analytics is a process of analyzing and exploring a huge amount of unstructured data assisted by software which can recognize patterns, concepts, keywords, topics, and other attributes in the text. Text analytics has become further practical for data scientists and users owing to the advancement of deep learning algorithms and big data platforms that can analyze huge sets of unstructured text.

Analyzing and mining text helps businesses to find potentially valuable business insights in consumer’s emails, corporate documents, call center logs, social network posts, medical records, verbatim survey comments, and other resources of text-based data. Gradually, text analytics

Text analytics is a process of analyzing and exploring a huge amount of unstructured data assisted by software which can recognize patterns, concepts, keywords, topics, and other attributes in the text. Text analytics has become further practical for data scientists and users owing to the advancement of deep learning algorithms and big data platforms that can analyze huge sets of unstructured text.

Analyzing and mining text helps businesses to find potentially valuable business insights in consumer’s emails, corporate documents, call center logs, social network posts, medical records, verbatim survey comments, and other resources of text-based data. Gradually, text analytics competences are also being integrated into virtual agents and chatbots that organizations deploy to offer automated responses to consumers as a part of their marketing, customer service, and sales operations.

Areas of Text Analytics

• IE (Information Extraction): Information extraction is the method of automatically accessing structured data from unstructured data. The action involves NLP (Natural Language Processing).

• DM (Data Mining): DM looks for data patterns. It can be more defined as the recovery of hidden info from data. It enables industries to make positive decisions depending on knowledge and answer business queries.

• NLP (Natural Language Processing): The main purpose of NLP is to provide the system in the data recovery phase as an input.

• IR (Information Retrieval): Information retrieval is called as an extension to document abstraction. Information retrieval systems aid in narrowing down the collection of records which are associated with a particular problem.

How Text Analytics Works?

Text analytics is similar in nature to data analytics, but with emphasize on text instead of more structured data. Though, the first step in the text analytics process is to structure and organize the data in some manner so it can be exposed to both quantitative and qualitative analysis.

Doing so usually includes the use of NLP (Natural Language Processing) technology that applies computational semantics principles to interpret and parse text sets.

The upfront work comprises clustering, tagging, and categorizing text; creating taxonomies, and mining information about things such as relationships between data entities and word frequencies. Analytical simulations are then run to produce findings which can help to drive operational actions and business strategies.

In the past few years, natural language processing algorithms primarily depended on rules-based or statistical models which provided direction on what to search for data sets. In 2010, however, the deep learning models which work in a less controlled way developed as an alternative method for text analytics and also for other improved analytics applications including large data collections.

As a result, the text analytics tools are currently better armed to uncover fundamental similarities and relations in text data, although data scientists do not have a better understanding of what they are expected to find at the beginning of a project.

Benefits of Text Analytics

• Deploying text analytics to improve insight into consumer sentiment can aid organizations to detect the product and several business issues and then address them before they become serious issues which affect sales.
• Analyzing the text in consumer communications and reviews can also recognize desired new features to assist strengthen product offerings.
• Text analytics can also help to predict consumer churn, aiding organizations to take action to head off possible defections to business competitors as a part of their consumer and marketing relationship management program.
• Risk management, fraud detection, web content management, and online advertising are other functions that can benefit from the implementation of text analytics tools.
• In the healthcare industry, the technology may be able to aid diagnose medical and illness conditions in patients on the basis of symptoms they report.

Text Analytics Challenges and Issues

• Text analytics can be challenging because the text is often indefinite, contradictory, and inconsistent. Efforts to evaluate it are more complicated by uncertainties that result from differences in semantics and syntax, and the use of sarcasm, slang, technical and regional language specific to the individual vertical organization. As a result, text analytics algorithms essential to train to analyze such inconsistencies and ambiguities when they tag, categorize, and summarize a collection of data.
• Furthermore, the deep learning models implemented in a number of text analytics applications need large amounts of processing power and training data, which can make them exclusive to run. Integral bias in data sets is another main issue which can lead deep learning tools to generate defective results if data scientists do not identify the biases over the model development process.

Applications of Text Analytics

• Analyzing consumer review responses

Open ended survey queries will aid the respondents to give their opinion or view without any restrictions. This will help to identify more about the consumers’ opinions than trusting on structured surveys. Text analytics can be used to analyze this information as a text.

• Automatic processing of emails, messages

Text Analytics is also mostly used to categorize the text. Text analytics can be used to sort the needless mail by using certain phrases or words. Such mails will automatically remove to spam. An automatic system of filtering and categorizing selected mails and sending it the consistent department is done using text analytics system.

• Analyzing insurance or warranty claims

In many organizations, information is composed mostly in the form of a text document. For example in healthcare industry the patient interviews can be described briefly in text format and the reports are also generated in the text form. These notes are nowadays together electronically so that it can be simply transferred into text analytics algorithms. Such records can then be useful to diagnose a genuine situation.

• Investigating participants by crawling their sites

Another significant application area of text analytics is processing the subjects of web pages in a specific domain. Through this method one can find out the most significant terms implemented in the website. With such a way, one can recognize the capabilities of the participants that can help you to provide business efficiently.