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Top 11 Natural Language Processing Applications

Natural language processing tools can help businesses analyze data and discover insights, automate time-consuming processes, and help them gain a competitive advantage.

Let’s take a look at 11 of the most interesting applications of natural language processing in business:

Sentiment Analysis
Chatbots & Virtual Assistants
Text Classification
Text Extraction
Machine Translation
Text Summarization
Market Intelligence
Auto-Correct
Intent Classification
Urgency Detection
Speech Recognition

1. Sentiment Analysis

Natural language understanding is particularly difficult for machines when it comes to opinions, given that humans often use sarcasm and irony. Sentiment analysis, however, is able to recognize subtle nuances in emotions and opinions ‒ and determine how positive or negative they are.

When you analyze sentiment in real-time, you can monitor mentions on social media (and handle negative comments before they escalate), gauge customer reactions to your latest marketing campaign or product launch, and get an overall sense of how customers feel about your company.

You can also perform sentiment analysis periodically, and understand what customers like and dislike about specific aspects of your business ‒ maybe they love your new feature, but are disappointed about your customer service. Those insights can help you make smarter decisions, as they show you exactly what things to improve.

Try out this online sentiment analyzer to see how natural language processing sorts your text by emotions.
2. Chatbots & Virtual Assistants

Chatbots and virtual assistants are used for automatic question answering, designed to understand natural language and deliver an appropriate response through natural language generation.

Standard question answering systems follow pre-defined rules, while AI-powered chatbots and virtual assistants are able to learn from every interaction and understand how they should respond. The best part: they learn from interactions and improve over time.

These intelligent machines are increasingly present at the frontline of customer support, as they can help teams solve up to 80% of all routine queries and route more complex issues to human agents. Available 24/7, chatbots and virtual assistants can speed up response times, and relieve agents from repetitive and time-consuming queries.
3. Text Classification

Text classification, a text analysis task that also includes sentiment analysis, involves automatically understanding, processing, and categorizing unstructured text.

Let’s say you want to analyze hundreds of open-ended responses to your recent NPS survey. Doing it manually would take you a lot of time and end up being too expensive. But what if you could train a natural language processing model to automatically tag your data in just seconds, using predefined categories and applying your own criteria?

You might use a topic classifier for NPS survey responses, which automatically tags your data by topics like Customer Support, Features, Ease of Use, and Pricing. Give it a try and see how it performs!
4. Text Extraction

Text extraction, or information extraction, automatically detects specific information in a text, such as names, companies, places, and more. This is also known as named entity recognition. You can also extract keywords within a text, as well as pre-defined features such as product serial numbers and models.

Applications of text extraction include sifting through incoming support tickets and identifying specific data, like company names, order numbers, and email addresses without needing to open and read every ticket.

You might also want to use text extraction for data entry. You could pull out the information you need and set up a trigger to automatically enter this information in your database.

Keyword extraction, on the other hand, gives you an overview of the content of a text, as this free natural language processing model shows. Combined with sentiment analysis, keyword extraction can add an extra layer of insight, by telling you which words customers used most often to express negativity toward your product or service.
5. Machine Translation

Machine translation (MT) is one of the first applications of natural language processing. Even though Facebooks’s translations have been declared superhuman, machine translation still faces the challenge of understanding context.

However, if you’ve been an avid user of Google Translate over the years, you’ll know that it has come a long way since its inception, mainly thanks to huge advances in the field of neural networks and the increased availability of large amounts of data.

Automated translation is particularly useful in business because it facilitates communication, allows companies to reach broader audiences, and understand foreign documentation in a fast and cost-effective way.
6. Text Summarization