Understanding the techniques and applications of Text Analytics
Today an overwhelmingly big part of the world's information exists in a textual form: business records, government documents, legal acts, social media streams, clinical trials, medical archives, emails, and more.
Such a rapid increase of digital texts (across the Internet and on Intranets) causes the rising need for text analytics. It brings forward the question of finding a smarter way for reading and understanding texts, ultimately for deriving knowledge out of them.
With the many transformations the written word has gone through – from the oldest preserved inscriptions on clay tablets to the present astounding amount of documentation, stored in cloud systems (or other repositories), one thing remained unchanged: the information our textual sources contain is only as good as our ability and tools to extract and interpret it.
Now, let's take a deep dive into – What is text analytics?
According to Wikipedia, Text analytics is the process of transforming unstructured text documents into usable, structured data. Text analysis works by breaking apart sentences and phrases into their components and then evaluating each part's role and meaning using complex software rules and machine learning algorithms.
Decades ago, text analytics involved simple tasks like calculating word frequencies. Over the last few years, artificial intelligence technologies like natural language understanding (NLU) and machine learning, and techniques like deep learning have dramatically improved the effectiveness of text analytics.
Around 80% of data held within an organization is in the form of text documents—for example, reports, web pages, emails, call center notes, etc. Text is a key factor in enabling an organization to gain a better understanding of their customers' behavior.
Today, it is helping organizations with a means of understanding their customers better, helping them determine customer's demands and purchasing patterns by analyzing the data generated from various sources.
Here Are Some Examples of Text Analytics
• Fashion retailer H&M deployed text mining solutions for analyzing customer response on its social media channels. This enables the company to gain a better understanding of customer preferences and offer customized ads to target new customers for increasing profit avenues.
• Text mining/Analytics is gaining importance as it enables federal agencies and national security authorities to monitor the behavior of citizens for potential terrorist threats. As security agencies deploy text analytics solutions to analyze potential threat topics and objectionable materials mentioned in social media handles, the demand for text mining solutions will rise sharply over the projected timespan. Another factor contributing to the text analytics market share is the increasing deployment of text mining solutions for fraud detection.
• China Life Insurance adopted text mining software for information extraction from insurance claims. The technology enables the company to automate insurance claim handling and detect fraudulent claims by matching it with use cases.
Now take a Look at Some Techniques and Applications of Text Analytics
Text Analytics techniques can be understood as the processes that go into mining the text and discovering insights from it. These text mining techniques generally employ different text mining tools and applications for their execution.
This is the most popular text mining technique. Information exchange refers to the process of extracting meaningful information from vast chunks of textual data. This text mining technique focuses on identifying the extraction of entities, attributes, and their relationships from semi-structured or unstructured texts.
Whatever information is extracted is then stored in a database for future access and retrieval. The efficacy and relevancy of the outcomes are checked and evaluated using precision and recall processes.
Clustering is one of the most crucial text mining techniques. It seeks to identify intrinsic structures in textual information and organize them into relevant subgroups or 'clusters' for further analysis.
A significant challenge in the clustering process is to form meaningful clusters from the unlabeled textual data without having any prior information on them.
Cluster analysis is a standard text mining tool that assists in data distribution or acts as a pre-processing step for other text mining algorithms running on detected clusters.
Text summarisation refers to the process of automatically generating a compressed version of a specific text that holds valuable information for the end-user.
This text mining technique aims to browse through multiple text sources to craft summaries of texts containing a considerable proportion of information in a concise format, keeping the overall meaning and intent of the original documents virtually the same.
Text summarisation integrates and combines the various methods that employ text categorization like decision trees, neural networks, regression models, and swarm intelligence.
This is one of those text mining techniques that is a form of "supervised" learning wherein normal language texts are assigned to a predefined set of topics depending upon their content.
Thus, categorization or rather Natural Language Processing (NLP) is a process of gathering text documents and processing and analyzing them to uncover the right topics or indexes for each document. The co-referencing method is commonly used as a part of NLP to extract relevant synonyms and abbreviations from textual data.
Today, NLP has become an automated process used in a host of contexts ranging from personalized commercials delivery to spam filtering and categorizing web pages under hierarchical definitions, and much more.
Applications of Text Analytics/Text Mining
The rapidly growing technology is penetrating the industry. It is giving rise to several text mining applications. Here are a few text mining applications used across the globe today:
In many industries like the healthcare industry, managing a huge amount of textual information has become a problem. If you started building racks and kept all the documents related to healthcare on a single rack, scalable vertically, it would probably reach the moon. The amount of information gathered every single hour is huge.
All this data has to be stored in such a manner that the information can be retrieved as and when required. It may so happen, that there is an epidemic and hospitals need to coordinate to go through all their data to pinpoint the source or the first infected person.
Such a huge exercise would be impossible without the help of proper text analytics systems in place that would manage the data and information and keep them in a structured tree-like format. This would lead to people being able to access the data in any way they need- region-based, gender-based, disease-based, and more.
The inability to find important information quickly may cripple such organizations dealing with large volumes of text documents.
Social Media Analysis
There are many text mining tools designed exclusively for analyzing the performance of social media platforms. These help to track and interpret the texts generated online from the news, blogs, emails, etc.
Furthermore, text mining tools can efficiently analyze the number of posts, likes, and followers of your brand on social media, thereby allowing you to understand the reaction of people who are interacting with your brand and online content. The analysis will enable you to understand 'what's hot and what's not' for your target audience.
Customer Care Service
Text mining applications, particularly NLP, are finding increasing importance in the field of customer care. Companies are investing in text analytics software to enhance their overall customer experience by accessing the textual data from varied sources such as surveys, customer feedback, and customer calls, etc.
Text analysis aims to reduce the response time of the company and help address the grievances of the customers speedily and efficiently.
Text analytics backed by text mining techniques provides a tremendous opportunity for domains that gather a majority of data in the text format. Insurance and finance companies are harnessing this opportunity.
By combining the outcomes of text analysis with relevant structured data, these organizations are now able to process claims swiftly as well as to detect and prevent frauds.
One of the primary causes of failure in the business sector is the lack of proper or insufficient risk analysis. Adopting and integrating risk management software powered by text mining technologies such as SAS Text Miner can help businesses to stay updated with all the current trends in the business market and boost their abilities to mitigate potential risks.
Since text mining tools and technologies can gather relevant information from across thousands of text data sources and create links between the extracted insights, it allows organizations to access the right information at the right moment, thereby enhancing the entire risk management process.
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Text Analytics Market Overview
The text analytics market is poised to grow by USD 8.77 billion during 2020-2024, progressing at a CAGR of over 20% during the forecast period.
The global lockdown has not impacted on the text analytics market, as the operations in the IT industry are carried out generally by 'working from home' structure. And, as a result, the use of text analytics in its application industry has a constant demand across the world.
Moreover, text analytics has become helpful in the health care sector during the Coronavirus pandemic to explore the information about the Coronavirus. Text analytics is a software that helps to create proper text data from unstructured text from trends, uncover insights, and patterns.
The technology is expected to gain ground over the next few years owing to its ability to predict and forecast consumer behavior. The technology is used across an array of applications including brand-reputation management, market research, competitive intelligence, and customer service & support.
Major players in the market such as - Brandwatch, SAS, IBM Corporation, HP, and more; are focused on embedding text analytics capabilities like NLP across several enterprise applications for better business mechanisms.
So, from this blog, we've given you a very high-level overview of what is done in text analytics by going into very minimum depth. We hope this informative piece helped you understand the basics of text mining and its applications in the industry.