Top 10 Trending Applications of Natural Language Processing (NLP) In 2022
Natural Language Processing is an application of artificial intelligence and offers the facility of offering applications to companies that need to analyse their data reliably. This quality efficiently enables human-computer interaction and also allows for the analysis and formatting of large volumes of previously unused data. According to Statista, the Natural Language Processing (NLP) market is expected to grow 14 times in 2025 than it was in 2017. This means increasing from around 3 billion USD to 43 billion
Businesses need data but unfortunately, the more substantial part of the data which covers almost 80% of it, is unstructured and inaccessible. This is where Natural Language Processing (NLP) has come as a situation saver.
Why is NLP important?
Our language has so many variations and nuances that it can be pretty overwhelming for an individual or business to analyze. Every written or verbal expression adds some type of information that can be interpreted and value can be extracted from it. The problem is if you want to analyze a few thousand or million “conversations” — the job can be incredibly cumbersome. So, why is NLP important?
- To analyze large volumes of text or speech data
- To standardize a highly unstructured data pipeline
Top 10 Natural Language Processing Applications
1.Email Filtering:
You might have already noticed that whenever a mail arrives, it gets classified into the sections of primary, social, and promotions. And the best thing is that the spam emails are also filtered out to a separate section. Isn’t it amazing and beneficial at the same time? Yes, it is, and that’s all email filtering is. And I don’t have to tell you how much our daily tasks rely on this feature.
The emails are filtered using text classification, which is a natural language processing technique. And as you might have already guessed it. Text Classification is the process of classification of a piece of text into pre-defined categories. Another great example of text classification is the classification of news articles into various categories. Here are few resources to getting you started with text classification:
- A Comprehensive Guide to Understand and Implement Text Classification in Python
- Tutorial on Text Classification (NLP) using ULMFiT and fastai Library in Python
- Build Your First Text Classification model using PyTorch
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Survey Analysis
Companies use surveys as an important means of evaluating their performance. Be it getting feedback on the latest product launch or getting to know about the performance of its customer service, survey analysis plays a huge role in understanding the loop holes and helping the companies improve their products.
The problem arises when a lot of customers take these surveys leading to exceptionally large data size. All of it cannot be comprehended by the human brain. That’s where natural language processing enters the canvas. These methods help the companies to get accurate information about the customer’s opinion and improve their performance.
3.Voice Assistants –
I am sure you’re all used to Siri, Alexa, or Google Assistant. All these voice assistants use speech recognition, natural language understanding, and processing to understand the verbal commands of the user and perform actions accordingly. From the time they were introduced to now, they have transformed into a very reliable gadget.
They use a complex combination of speech recognition, natural language understanding, and natural language processing to understand what humans are saying and then act on it. The long term goal of voice assistants is to become a bridge between humans and the internet and provide all manner of services based on just voice interaction. However, they are still a little far from that goal seeing as Siri still can’t understand what you are saying sometimes!
4.Social Media Monitoring
More and more people these days have started using social media for posting their thoughts about a particular product, policy, or matter. These could contain some useful information about an individual’s likes and dislikes. Hence analyzing this unstructured data can help in generating valuable insights. Natural Language Processing comes to rescue here too.
Today, various NLP techniques are used by companies to analyze social media posts and know what customers think about their products. Companies are also using social media monitoring to understand the issues and problems that their customers are facing by using their products. Not just companies, even the government uses it to identify potential threats related to the security of the nation.
If you are also excited about leveraging the natural language processing for monitoring social media, then here are few articles to start your journey:
- Comprehensive Hands-on Guide to Twitter Sentiment Analysis with dataset and code
- Measuring Audience Sentiments about Movies using Twitter and Text Analytics
- Sentiment Analysis of Twitter Posts on Chennai Floods using Python
5.Language Translator
In earlier days, machine translation systems were dictionary-based and rule-based systems, and they saw very limited success. However, due to evolution in the field of neural networks, availability of humongous data, and powerful machines, machine translation has become fairly accurate in converting the text from one language to another.
Today, tools like Google Translate can easily convert text from one language to another language. These tools are helping numerous people and businesses in breaking the language barrier and becoming successful.
6.Search Autocorrect and Autocomplete
It’s a wonderful application of natural language processing and a great example of how it is affecting millions around the world, including you and me. Search autocomplete and autocorrect both help us in finding accurate results much efficiently. Now, various other companies have also started using this feature on their websites, like Facebook and Quora.
The driving engine behind search-autocomplete and autocorrect are the language models. You can read more about language models in this article: A Comprehensive Guide to Build your own Language Model in Python
7.Digital phone calls
Automated systems direct customer calls to a service representative or online chatbots, which respond to customer requests with helpful information. This is a NLP practice that many companies, including large telecommunications providers have put to use. NLP also enables computer-generated language close to the voice of a human. Phone calls to schedule appointments like an oil change or haircut can be automated, as evidenced by this video showing Google Assistant making a hair appointment.
8.Text analytics
Analysis of these interactions can help brands determine how well a marketing campaign is doing or monitor trending customer issues before they decide how to respond or enhance service for a better customer experience. Additional ways that NLP helps with text analytics are keyword extraction and finding structure or patterns in unstructured text data.
While sentiment analysis sounds daunting to brands–especially if they have a large customer base–a tool using NLP will typically scour customer interactions, such as social media comments or reviews, or even brand name mentions to see what’s being said. Analysis of these interactions can help brands determine how well a marketing campaign is doing or monitor trending customer issues before they decide how to respond or enhance service for a better customer experience.
9.Neural Machine Translation –
This is one of the oldest applications of NLP which is relevant even in 2021. In this, a machine translation uses a neural network to translate low impact content and speed up communication with its partners. A bidirectional recurrent network called an encoder processes a source sentence into vectors for another recurrent neural network, called the decoder. This helps to predict words in the target language as we see in Google Translate.
10.Targeted Advertising –
Leads generation stays at the core of businesses. This is the main reason they want to reach out to the maximum number of audience. Natural Language Processing is an amazing resource for placing the right advertisement, in the right place, at the right time. This is done through keyword analysis, browsing patterns of users over the internet, emails, or social media platforms. Text mining tools are leveraged to perform these tasks.
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