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.
Natural Language Processing, also shortened to NLP, is a subfield of artificial intelligence (AI). It 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.
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.
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:
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:
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:
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:
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 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 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 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 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.
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.
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:
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.
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.
February 26th, 2020