According to a study by EMC, only 1% of the data generated is utilized. We have normalized the practice of storing and analyzing structured data – numerical and some alpha-numeric information that can be easily put into rows and columns. However, an increasing number of businesses are coming to realize the potential of unstructured data, composed of texts, audio, and video-based information. The evolution of data analytics in terms of augmenting business decisions as well as improving our general understanding of the world depend on some key steps.
Step 1: Making sense of our language (natural language) in terms of machine utility while accounting for human errors, idiosyncrasies, dialects, and sentiments.
Step 2: Organizing the unstructured pool of data into something more analysis-friendly in a meaningful and relevant manner.
Making a case for text analytics
An application developer may want to understand the general observation of the users about the app or delve deep into some specific criticism. A text analysis programme can be deployed to pick out the important comments saving the company hundreds of hours of meaningless browsing through reviews.
The whole gamut of written language inputs generated by humans in the form of search queries, communications (like the 300 billion emails that are sent everyday), social media feeds, hold a crucial place in an enterprise’s efforts to understand its customers. Using the help of machine learning seems to be the only sane way to make any headway through this massive pile of data.
Text analytics tools extract patterns after reviewing the literature looking for themes, sentiments, and concepts alongside testing a given hypothesis. These tools require very little human assistance as they execute tasks with the help of NLP and machine learning.
A report published on June 7, 2016 revealed that a leading tech company managed to identify anonymous pancreatic cancer patients by analyzing search queries with a staggeringly low false-positive rate. The controversial success of this innovative attempt speaks for the efficiency and prospects of text analytics. A vertical persuasion regarding the method of this achievement will most certainly help in a better understanding of how text analysis works.
The text analytics workflow
Like other disciplines of analytics, a database is required for text analytics. This database is a collection of unstructured texts and documents. The data is converted into structured data with the help of Matrix mapping. This structured database (Term-Document Matrix) is then analyzed using a machine learning tool of interest.
Structured data in this case, is numeric data derived from textual data in columns. And source document information placed in rows. The numeric data are then assigned in accordance with the qualitative and quantitative aspects of the occurrence.
Structured data processing
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- This structured data is then reduced in terms of dimensions for a relatively fast and smooth mining operation.
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- Variants of meanings in the forms of words, expressions, and phrases are then marked as the same depending on the scenario.
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- A spelling and grammar check is done for increasing the accuracy and reliability of the database.
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- Homonyms are identified and marked as different by using machine learning tools.
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- Low frequency is marked as insignificant and termed as noise.
After preparation of the database statistical analysis is performed as required and relevant data mining tools and NLP methods are deployed in search of answers.
Fields of application
The data dependency of modern industries initiated a surge of innovations in the field. In the health industry, text analytics is used as a tool for diagnostics. Pathological and psychological conditions ranging from cardiac disorders to depression can be diagnosed with the help of text mining. For businesses, text analytics is becoming a key to understand client sentiments and consumer psychology. As a result, text analytics has become the weapon of choice for marketing and recruitment operations. It is quite presumable, what benefits TA can shower on the fintech and banking sectors.
Let us finish with the story of two chatbots named Tay and Zo. Tay was designed to become smart by talking to people on the internet and ended up going rogue. The bot was soon put down for its abusive behavior. Zo was launched after the failure of Tay with a relatively polished set of algorithms. The bot succeeded in generating innovative conversations with people of all classes with a greater degree of success. This goes on to showcase the bold strides made towards better natural language processing as well as understanding.
We at Algoscale have been solving critical customer problems in the field of text analytics.
To learn more, contact us at askus@algoscale.com
