Important Use Cases of NLP

In today’s world we are generating large amount of data every second. while tweeting, chating, writing or even speaking, we are fabricating corpse of data. Most of the data is in textual and unstructured form. Hence to make this data understandable by computer, we need to process it. NLP technique helps us in processing the data and helps us to get useful insights from it.

Before diving into NLP, let’s have a look into the amount of data generated every second.

On average, Google now processes more than 40,000 searches every second (3.5 billion searches per day)!
While 77% of searches are conducted on Google, it would be remiss not to remember other search engines are also contributing to our daily data generation. Worldwide there are 5 billion searches a day.

Our current love affair with social media certainly fuels data creation. According to Domo’s Data Never Sleeps 5.0 report, these are numbers generated every minute of the day:

Snapchat users share 527,760 photos
More than 120 professionals join LinkedIn
Users watch 4,146,600 YouTube videos
456,000 tweets are sent on Twitter
Instagram users post 46,740 photos

With 2 billion active users Facebook is still the largest social media platform. Let that sink in a moment—more than a quarter of the world’s 7 billion humans are active on Facebook! Here are some more intriguingFacebook statistics:

1.5 billion people are active on Facebook daily
Europe has more than 307 million people on Facebook
There are five new Facebook profiles created every second!
More than 300 million photos get uploaded per day
Every minute there are 510,000 comments posted and 293,000 statuses updated

Even though Facebook is the largest social network, Instagram (also owned by Facebook) has shown impressive growth. Here’s how this photo-sharing platform is adding to our data deluge:

There are 600 million Instagrammers; 400 million who are active every day
Each day 95 million photos and videos are shared on Instagram
100 million people use the Instagram “stories” feature daily

We leave a data trail when we use our favorite communication methods today from sending texts to emails. Here are some incredible stats for the volume of communication we send out every minute:

We send 16 million text messages
There are 990,000 Tinder swipes
156 million emails are sent; worldwide it is expected that there will be 2.9 billion email users by 2019
15,000 GIFs are sent via Facebook messenger
Every minute there are 103,447,520 spam emails sent
There are 154,200 calls on Skype

Now that you are armed with these numbers, consider all the ways that you generate data as you go about your day. Once you are aware of all the data you create as a single individual, you can start to imagine just how much data we collectively generate every single day. Do we know the fact that out of total available data in the world, about 90 percent is generated over the last two years alone.

Now as we have already understood the amount of raw data presence over internet, It is nearly impossible for us to analyze organize or record such amount of data hence NLP make this job easier for us.

Natural processing is a branch of Artificial intelligence. Where we are enabling machines or computers to interact and react like humans. NLP entitle computers to understand the text, speech, interpret it and determine sentiments. And give the desired results.

According to wikipedia
“Natural language processing (NLP) is a subfield of computer science, information engineering, and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data.”

NLP can organize unstructured data and perform several automated tasks such as automatic summarization, sentiments analysis, speech recognition, etc.

Siri (Apple) and google assistant are the most popular and relatable use cases of the NLP, they understand our commands and reply back effectively.

Some more Real life use cases or examples of natural language processing:

Creditworthiness assessment:

Banks can assess the creditworthiness of the client with less or no credit history by using NLP. They can also find out if the person is suitable to receive financial credit or not.

NLP algorithms analyze thousands of clients on the basis of their digital footprints. Such as demographics, social media activities and browsing history, network and connections. And get insights into the client’s personality traits. NLP generate credit score on the basis of these categories and predict the future activity and behavior of the client.

The permission, to dig down into users personal space and get useful insights, is granted by the user. All the information is confidential and can not be shared with the third party.

Chatbots:

Chatbots are quite prevalent in the business field by now. they are smart and can recognize human emotions and behavior. they can book an appointment, strike a conversation, target prospects and can give a personalized experience to the customer by addressing them individually. it avoids complexities of human to human conversation.

Sentiment Analysis:

Sentiment analysis is also known as opinion mining. industries are using this technique to find out what the consumer is feeling about their brand. are they happy, sad, neutral, annoyed or angry with the brand? this technique analyzes social media, news, blogs, articles etc and gives feedback to the company. marketers use these feedbacks to upgrade and optimize their product quality and sales strategies.

Hiring and recruitment:

Nowadays HR professionals are using NLP based software to speed up the candidate search. It helps them to filter out relevant resume by creating bias proof and gender-neutral job descriptions. NLP uses semantic categorization to tweak job descriptions in a way that can increase the no. of applicants.
Semantic means relating to meaning. Words can have many meanings. Semantic Classification tools review the job description to create relationships between the words in it hence the candidate can get the clear idea of the job profile and industry.

Advertising and Marketing:

NLP helps brands to recognize potential customers by analyzing digital footprints such as social media, google search, browsing behavior of the population. it helps companies to place their ads wisely and spend ad budget effectively. you must have seen the product suggestion ads on the web page, you visit, inspired by your previous buy or search history.

NLP is also helping marketers to keep and on their competitors by crawling through millions of article, blogs, and social media trends.

Cognitive Assistant:

IBM developed a cognitive assistant which is a personal search engine. it learns and reminds all the personal information about you such as name, your favorite song or anything that you need.