Natural Language Processing (NLP) is a subfield of artificial intelligence (AI) that helps machines understand human language. NLP aims to simplify natural language so that machines can process and analyze language in any given context.
Take this sentiment analyzer tool, which uses NLP to detect emotions in text. Sentiment analysis is one of the most basic challenges of NLP and often used by businesses to detect brand sentiment on social media.
Read on to learn more about NLP applications, how natural language processing works, and easy-to-use NLP tools.
- How Does NLP Work?
- Natural Language Processing Examples
- Business Applications of NLP
- Top NLP Tools To Help You Get Started
How Does NLP 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 step to understanding natural language is to transform data into something that machines can interpret (this stage is called data preprocessing).
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. There are several techniques that can be applied to clean a dataset and make it more organized, such as breaking up text into small semantic units (tokenization), categorizing words as nouns, verbs, adjectives, adverbs, or pronouns (part of speech tagging), reducing words to its root form (stemming), removing stop words, and identifying relationships among different entities.
Once the dataset is ready, it’s time to move onto the next step: building an NLP model, and training it so it can interpret natural language and perform specific tasks.
There are two main approaches to solve NLP problems:
- 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.
- 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: What Is NLP Used For?
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 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, etc, businesses can gain valuable insights about how customers perceive their brand.
Take these product reviews, for example:
- What used to be a great product is not anymore”
- Easy-to-use software for all skill levels”
A sentiment classifier can understand the nuance of each opinion and automatically tag the first sentence 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 just a few seconds, saving an enormous amount of time and resources. It can also analyze data in real-time, which is particularly useful for monitoring social media. 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 technique 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 in software and receive a series of customer support tickets that mention technical, usability, and feature issues, in which 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, for example, by tagging them as Interested, Need Information, Unsubscribe, Bounce, etc. The tag Interested, for example, could help you spot a potential sale opportunity as soon as an email arrives in your inbox!
Another use of NLP is text extraction, which consists of pulling out specific pieces of data that are already present in a text. 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.
Other useful tasks of natural language processing include:
- Machine Translation: this was one of the first problems addressed by NLP researchers. Online translation tools (like Google Translate) use different NLP 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.
- Automatic Summarization: creates a shorter version of a text, with the most important information.
- Topic Modeling: similar to topic classification, this technique finds relevant topics in a text by grouping texts with similar words and expressions. Since it doesn’t require predefined tags or tagged data, it’s a good option for exploratory analysis, when you are not yet familiar with your data.
- Natural Language Generation (NLG): this NLP task consists of analyzing unstructured data, and using it as an input to automatically create content. In business, it can be used to generate automated answers in customer support, for example.
Business Use Cases & Applications of NLP
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:
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.
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.
At Retently, they use a topic classifier to tag each open-ended response with categories like Product UX, Customer Support, Ease of Use, etc. Then, they cross this data with the types of scorers (Promoters, Detractors, and Passives), to see which tags are most prevalent with each group of customers:
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:
- Google Cloud NLP
- IBM Watson
- Amazon Comprehend
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:
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.
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