Natural Language Processing for Enhanced Data Analytics

Sept 24, 2020

An IDC Digital Universe Study reports that by 2020, for every human in the world, approximately 1.7 megabytes of new information will be created every second; that’s around 44 trillion gigabytes of digital knowledge worldwide.

This immense pool of big data comprises mainly of text information like customer and sales information, transactional data, research along with external open-sourced information and social media. The data is primarily unstructured and constantly growing. Also, it is in natural language, which means it comprises of words that we use in our daily lives. Hence, Natural Language Processing (NLP) of big data is poised to be the next big leap in data analytics and has huge growth potential.

 

Before embarking on the relevance of NLP in data analytics, let’s first learn the meaning of NLP. NLP is a type of Artificial Intelligence (AI) that assists machines in reading text through simulation of humans’ ability to understand language. NLP makes use of various methods which include linguistics, semantics, statistics and machine learning for extracting entities, relationships and understanding context. This ensures that whatever is written or spoken is understood comprehensively. NLP assists computers in understanding sentences as spoken or written by humans rather than merely single words or combinations. It deploys numerous methodologies for deciphering complexities in language including automatic summarization, tagging of parts-of-speech, disambiguation, extraction of entities and relations along with comprehending and recognizing natural language.

Role and Uses of NLP in Big Data

Major organizations in sectors such as medical, legal, pharmaceutical, education, and so on generate large amounts of data that are archived on a daily basis. These can be in the form of documents, customer inputs, sales information, etc. These data are mainly texts, so, NLP is essential for obtaining valuable results from the analysis – predictive, real-time, or historical.

NLP for Interaction

The most common example of NLP-based interactive applications can be seen in the form of smartphone assistants such as Apple’s Siri, online banking, and self-service tools in retail and certain automatic translation programs. The users pose questions in casual language and get prompt and accurate responses. This is indeed a win-win scenario for both the customer and the company. The customer benefits by being able to communicate easily with the company and the company benefits by saving on calls made by employees.

NLP in Business Intelligence

NLP can be used in big data to quickly extract relevant information and also obtain a summary of the contents of the documents that are present in large catalogs or datasets for collective insight. The users need not select or have an understanding of the right keywords for extracting what they are searching, instead, they can use search engine queries formulated in their own words for interacting with the content. The fast information retrieval process enables the hastening of all the other processes linked to it and permits real-time actionable business intelligence.

Sentiment Analysis

The growing number of online customers have made social channels a rich source of information. Through sentiment analysis, organizations can find out what is thought of or talked about their brands and products online. They can know how their users feel about their service, product, and idea. Hence, sentiment analysis is a strong tool for discovering information regarding the market, customers – present and prospective. Businesses can benefit from a detailed demographic profile of their customers, their preferences, needs, habits, and so on. This information can then be used for developing products, business intelligence, and market research.

NLP for fighting against pandemic such as COVID-19 Crisis

AI along with NLP are playing major roles in the fight against the COVID-19 pandemic. Biometric-aided computer vision is being used for identifying people who are not wearing masks. NLP-powered chat programs are helping in detecting the outbreak in early stages along with monitoring to minimize the spread of the virus. IBM has launched IBM Watson for Citizens which is a combination of Watson Assistant, NLP technology, and Watson Discovery. According to IBM, out of the numerous phone calls that the government is receiving related to Coronavirus, most are of the common type and can be handled easily by AI.

Moreover, among the first public alerts regarding the Coronavirus outbreak was from the automated HealthMap system at the Boston Children’s Hospital. The system uses NLP for scanning online news and social media reports. It had delivered an alert regarding unidentified pneumonia cases in Wuhan at 11:12 pm (EDT) on December 30, 2019.

It’s clear from the points highlighted above that NLP is playing a vital role in connecting end users with relevant information resulting in cost savings and human efforts. A business user must certainly give preference to data platforms that support NLP over others.

Rawcubes’ DataBlaze is one such data platform that enables easier and real-time retrieval of data through its knowledge graph and supports both SQL and NLP for querying data.

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