Natural Language Processing (NLP) in Everyday Life
While Natural Language Processing can still seem like a fancy term, we’ve been enjoying the benefits of NLP for long now. For beginners in this field, we can start with learning about the real-world applications so that we’d gain a better understanding of what NLP does, what can be expected out of it, and where it’s been quite successful.
Natural Language Processing is a branch of Artificial Intelligence that is used for going through raw data (usually in big amounts), processing it, and extracting the information we want out of the same. This way, AI saves us time and effort by presenting to us only what we consider relevant information. It’s up to us to then decide what to do with the information we have at hand, depending on the users’ projects and goals. NLP accomplishes this using machine learning models.
Common Real-Life Examples of Natural Language Processing: You don’t have to be a business owner or a techie to have bumped into these examples of natural language processing in daily life. If you use Internet, social media, email, and online search, you’d have come across most of these natural language processing applications even if you’d not recognized them.
Chatbots and Voice-Assistants: How often have you wanted to reach out to an organization, and you’ve chatted with a machine instead of a human being, which would provide you with guidance and help and seek to solve your problems by itself? If the experience had been good or smooth and your problems had been easily resolved, good, you’ve interacted with a good chatbot. If the experience added to your frustration, sorry. Also, do you use Alexa or Siri? All these interactions you’ve had with machines either in the form of voice or text are examples of Natural Language Processing at work.
Spam Filter: Wonder how spam mail automatically goes into the spam section, thereby saving you time and keeping you content with the use of email? How does a machine know spam is spam, though? That’s again, the power of machine learning and natural language processing. The algorithm is designed to look for patterns that are usually found in spam email, it is both designed brilliantly and is also updated often, so when it sees an email in your inbox, it can identify and label content as spam or promotion or primary email (email you need to see).
Auto-correct: Sometimes helpful and sometimes annoying, sounds like a supportive friend or family member? No, it’s auto-correct that I’m talking about. As a writer, I spend most of my time reading, making notes, and writing, sorry, by “writing”, I meant “typing”. Those red lines on MS-Word and even PyCharm help me notice mistakes I’ve made so that I can correct them before sharing my content and prevent getting embarrassed. And MS-Word’s auto-correct — what would I do without you? I honestly don’t want to spend 20% of my time correcting typos. Can you relate? And the number of times auto-correct on my smartphone changes words to things I don’t mean — God! How does the machine know what’s a proper word and what is not, when to notify the user, and when to go ahead and change the spelling automatically? All that is because of NLP.
Search Engine Optimization (SEO): Are you a blogger or a content writer? You must have then come across the term “search engine optimization”. Good content alone wouldn’t take your write-up to the audience you target. There are also other aspects you should devote your time to, like picking the right keywords, choosing a highly appropriate heading or title, making sure your article isn’t too long or too short, etc. These choices help boost your write-up as highly appropriate or relevant. That’s how search engines know what content to offer the user. Why do some articles scale on top of the search results, and some others, even though the content is really well-written, don’t show up at all? The answer: keywords. The means: text classification, text mining, natural language processing, and machine learning. (There’s more than just keywords, though, but we’ll save that for another day.)
Sentiment Analysis Among Users and Customers: Have you heard of this recorded voice message, “This call will be recorded for quality and training purposes” before you get to speak to a customer support agent? Have you ever wondered what will they do with so many recorded phone calls, when you and I have not enough space in our phones to even save screenshots and memes? A waste of time, no? Actually not. Sentiment analysis is the process of going through data and analyzing the user’s sentiments and emotions based on the content they share. Speech recognition and named-entity recognition would help assess how a customer feels, and whether their opinion is positive or negative or neutral. A smart company can use this super power to rebuild their reputation even if it’s been really bad for a while.
Improving Customer’s Experience on E-Commerce Platforms: Do you make a few searches here and there on the Internet and on e-commerce platforms, and the notice that the site offers you suggestions about products very similar to what you were looking for and also sending you alerts on discounts in that line of products? Improving user experience is a major goal for all businesses. While your local shopkeeper could give you brand B when you ask for brand A or try to promote a new brand, stating its benefits and price, knowing your needs and expectations, artificial intelligence helps businesses do the same for millions of customers without involving direct human-to-human interaction.
Marketing is a key area where natural language understanding plays a significant role these days. NLP and/or NLU when used effectively can be extremely powerful. Social media platforms can be used as well misused by those who know the techniques. Politicians can make themselves appear highly concerned about the public by studying and understanding what masses of people want, how they feel, and what appeals to them. Businesses can grow to reach greater heights, by not only appealing to their loyal customers but also attracting new customers. All this is possible with social media marketing, natural language processing, data analysis, data analytics, and machine learning.
Technology presents us with power that is constantly updated and ever-evolving. It’s up to us to learn skills and employ tools to get the best out of it. How do you think you can leverage the power of natural language processing?
