We send messages and have conversations on social media every single day. That’s a lot of data generation, and when you add to that, surveys, customer feedback, online reviews, and so on, it’s easy to see why businesses get inundated with huge amounts of unstructured data.

Given that the majority of this data is text-heavy and, in most cases, unstructured, businesses need an effective way to sort it if they’re ever going to get any use out of it.

That’s where Natural Language Processing (NLP) comes in. Read on to learn more about NLP applications, how natural language processing works, and discover easy-to-use NLP tools.

• What Is Natural Language Processing
• How Does Natural Language Processing Work?
  • Data Pre-Processing
  • Natural Language Processing Algorithms
• Natural Language processing Examples
  • Automatically Analyzing Customer Feedback
  • Automating Tasks in Customer Support
• Natural Language Processing Tools
• Final Word on Natural Language Processing

What Is Natural Language Processing?

Natural Language Processing, also shortened to NLP, helps machines process and understand the human language in any given context so that they automatically can carry out repetitive tasks such as machine translation, summarization, ticket classification, and more. 

Take this sentiment analysis, for example, which uses natural language processing to detect emotions in text. This classification task is one of the most basic challenges of NLP and often used by businesses to detect brand sentiment on social media. 

How Does Natural Language Processing Work?

Human language is complex, ambiguous, disorganized, and diverse. There are more than 6,500 languages in the world, all of them with their own syntactic and semantic rules. The first involved in helping machines to understand natural language is to transform data into something that they can interpret. 

This stage is called data pre-processing.

Data Pre-Processing

In NLP, syntax and semantic analysis are key to understanding the grammatical structure of a text and identifying how words relate to each other in a given context. But, transforming text into something machines can process is complicated. 

There are several techniques that can be used to ‘clean’ a dataset and make it more organized, including:

• Tokenization: breaks down text into smaller semantic units or single clauses
• Part-of-speech-tagging: marking up words as nouns, verbs, adjectives, adverbs, pronouns, etc 
• Stemming and lemmatization: standardizing words by reducing them to their root forms
• Stop word removal: filtering out common words that add little or no unique information, for example, prepositions and articles (at, to, a, the).

Natural Language Processing Algorithms

Once the dataset is ready, it’s time to move onto the next step: building an NLP algorithm, and training it so it can interpret natural language and perform specific tasks.

There are two main algorithms you can use  to solve NLP problems:

1. A rule-based approach. Rule-based systems rely on hand-crafted grammatical rules that need to be created by experts in linguistics, or knowledge engineers. This was the earliest approach to crafting NLP algorithms, and it’s still used today.
2. Machine learning algorithms. Machine learning models, on the other hand, are based on statistical methods and learn to perform tasks after being fed examples (training data). 

Natural Language Processing Examples

Natural Language Processing enables you to perform a variety of tasks, from classifying text and extracting relevant pieces of data, to translating text from one language to another and summarizing long pieces of content.

Text Classification 

Text classification is one of the most basic NLP tasks and consists of assigning categories (tags) to a text, based on its content. Classification models can serve different purposes, for example: 

• Sentiment analysis: the process of analyzing emotions within a text and classifying them as positive, negative, or neutral. By running sentiment analysis on social media posts, product reviews, NPS surveys, and customer feedback, businesses can gain valuable insights about how customers perceive their brand.Take these Zoom customer and product reviews, for example: Equipped with natural language processing, a sentiment classifier can understand the nuance of each opinion and automatically tag the first review as Negative and the second one as Positive. Even though you could process this data manually, the great thing about sentiment analysis is that it allows you to analyze a whole batch of data in a matter of minutes, saving an enormous amount of time and resources.You can also analyze data in real-time, by connecting NLP tools like MonkeyLearn to your data sources, which is particularly useful for monitoring social media comments and online reviews. Imagine there’s a spike in negative comments about your brand on social media; sentiment analysis tools would be able to detect this immediately so you can take action before a bigger problem arises.
• Topic classification: this natural language processing example consists of identifying the main themes or topics within a text and assigning predefined tags. For training your topic classifier, you’ll need to be familiar with the data you’re analyzing, so you can define relevant categories. For example, you might work for a software company, and receive a lot of customer support tickets that mention technical issues, usability, and feature requests.In this case, you might define your tags as Bugs, Feature Requests, and UX/IX.

• Intent detection: this classification task consists of identifying the purpose, goal, or intention behind a text. It’s an excellent way of sorting outbound sales email responses by Interested, Need Information, Unsubscribe, Bounce, etc. The tag Interested could help you spot a potential sale opportunity as soon as an email enters your inbox!

Text Extraction

Another example of NLP is text extraction, which consists of pulling out specific pieces of data that are already present in a text. It’s a perfect way to automatically summarize text or find key information. The most common examples of extraction models are:

• Keyword extraction: automatically extracts the most important words and expressions within a text. This can provide you with a sort of preview of the content and its main topics, without needing to read each piece. Check out this feature request, below, processed with MonkeyLearn’s public keyword extractor: 

• Named Entity Recognition (NER): this technique allows you to extract the names of people, companies, places, etc.

Machine Translation

This was one of the first problems addressed by NLP researchers. Online translation tools (like Google Translate) use different natural language processing techniques to achieve human-levels of accuracy in translating speech and text to different languages. Custom translators models can be trained for a specific domain to maximize the accuracy of the results. 

Topic Modeling

Topic modeling is similar to topic classification. This example of natural language processing finds relevant topics in a text by grouping texts with similar words and expressions. 

Since you don’t need to create a list of predefined tags or tag any data, it’s a good option for exploratory analysis, when you are not yet familiar with your data.

Natural Language Generation (NLG)

Natural language generation, NLG for short, is a natural language processing task that consists of analyzing unstructured data and using it as an input to automatically create content. 

It can be used to generate automated answers, write emails, and even books!

Natural Language Processing Applications

Natural language processing allows businesses to make sense of all sorts of unstructured data ― like emails, social media posts, product reviews, online surveys, and customer support tickets ― and gain valuable insights to enhance their decision-making processes. Companies are also using NLP to automate routine tasks, reducing times and costs, and ultimately becoming more efficient. 

Here are some examples of how businesses are putting NLP into practice: 

Automatically Analyzing Customer Feedback

Analyzing customer feedback is essential to know what clients think about your product. However, this data may be difficult to process. NLP can help you leverage qualitative data from online surveys, product reviews, or social media posts, and get insights to improve your business.

An interesting example of this is Retently, a SaaS platform for online surveys, that uses MonkeyLearn to analyze NPS responses.

Let’s give you some context, first. NPS surveys are used by companies to measure how loyal their customers are. First, customers are asked to score a company from 0 to 10 based on how likely they are to recommend it to a friend (low scorers are categorized as Detractors, average scorers as Passives and high scorers as Promoters); then, an open-ended follow-up question asks customers the reasons for their score.

Retently uses a topic classifier to tag each open-ended response with categories like Product UX, Customer Support, Ease of Use, etc. Then, they further categorize this data into Promoters, Detractors, and Passives, to see which topics are most prevalent within each group of:

As you can see, in the graph above, the results show that customers are highly satisfied with aspects like Ease of Use _and _Product UX (since most of these responses are from Promoters), while Product Features definitely deserve immediate attention and improvement as it’s what most detractors are mentioning in their responses.

Automating Tasks in Customer Support

Businesses are using NLP models to automate tedious and time-consuming tasks in areas like customer service. This results in more efficient processes, and agents with more time to focus on what matters most: delivering outstanding support experiences.

Customer service automation powered by NLP includes a series of processes, from routing tickets to the most appropriate agent, to using chatbots to solve frequent queries. Here are some examples:

• Text classification models allow companies to tag incoming support tickets based on different criteria, like topic, sentiment, or language, and route tickets to the most suitable pool of agents. An e-commerce company, for example, might use a topic classifier to identify if a support ticket refers to a shipping problem, missing item, or return item, among other categories:

• Classifiers can also be used to detect urgency in customer support tickets by recognizing expressions such as ‘ASAP, immediately, or right now’, allowing agents to tackle these first. Here’s an example of how you can use MonkeyLearn’s urgency detector to spot an issue that needs to be solved right away: 

• Customer support teams are increasingly using chatbots to handle routine queries. This reduces costs, enables support agents to focus on more fulfilling tasks that require more personalization, and cuts customer waiting times.

Top NLP Tools to Help You Get Started

Natural language processing is one of the most complex fields within artificial intelligence. But, trying your hand at NLP tasks like sentiment analysis or keyword extraction needn’t be so difficult. There are many online NLP tools that make language processing accessible to everyone, allowing you to analyze large volumes of data in a very simple and intuitive way. 

SaaS platforms are great alternatives to open-source libraries, since they provide ready-to-use solutions that are often easy to use, and don’t require programming or machine learning knowledge. These tools are also great for anyone who doesn’t want to invest time coding, or in extra resources.

If you want to integrate tools with your existing tools, most of these tools offer NLP APIs in Python (requiring you to enter a few lines of code) and integrations with apps you use every day.

8 of the Best SaaS NLP Tools:

1. MonkeyLearn
2. Google Cloud NLP
3. IBM Watson
4. Lexalytics
5. Aylien
6. Amazon Comprehend
7. Clarabridge
8. MeaningCloud

The NLP tool you choose will depend on which one you feel most comfortable using, and the tasks you want to carry out. 

For example, MonkeyLearn offers a series of pre-trained models that are ready for you to start using right away. Once you get the hang of these models, you can build a customized machine learning model, which you can train with your own criteria to get more accurate results.

Check out these tutorials once you’re ready to start building your own custom model:

• How to build a custom classifier 
• How to build a custom extractor 

Final Words on Natural Language Processing

Natural language processing is one of the most promising fields within Artificial Intelligence, and it’s already present in many applications we use on a daily basis, from chatbots to search engines.  

Thanks to NLP, businesses are automating some of their daily processes and making the most of their unstructured data, getting actionable insights that they can use to improve customer satisfaction and deliver better customer experiences.

Despite being a complex field, NLP is becoming more and more accessible to users thanks to online tools like MonkeyLearn, which make it simple to create customized models for tasks like text classification and text extraction. 

Want to see how it works? Contact us and request a personalized demo from one of our experts! Or, get started right away and sign up to MonkeyLearn for free.