Use Cases and Benefits of Natural Language Processing (NLP)

Yasemin Ozturk Data Analyst, Kimola
Jun 29, 2022 - 8 min read

As a blend of Artificial Intelligence, Data Science, Computer Science, and Linguistics, Natural Language Processing (NLP) is an interdisciplinary structure with various uses. NLP practitioners can use NLP in customer-facing sectors, such as banking, insurance, health, advertising, public relations, and publishing. 

Let's look at the most common Natural Language Processing applications to understand how NLP is used and the power of NLP and its impact on our lives. You can also take a look at our previous article, Natural Language Processing and Its Applications, and learn about the history of NLP and the definition of NLP.

AutoCorrect and AutoComplete

These two are perhaps the most renowned and common forms of NLP in our daily lives. As we all know, Google begins to show us possible search terms after we type the first two or three letters of our query. Even when we make typos, Google Search corrects them and presents us with the most relevant results. It's convenient, isn't it? We all use it daily but rarely acknowledge how it makes our lives easier by helping us find the right results more efficiently than if we were entirely on our own.

Autocorrect technology

 

Language Translation

At some point, everyone has used Google Translate or other translation apps to find out what a particular word or phrase is in a different language. Machine Translation is the technique behind these applications that have helped many people and businesses overcome the language barrier. Machine translation automatically converts text from one language to another while keeping the meaning intact. In the early days, machine translation systems were dictionary and rule-based, which led to minimal success. However, thanks to the developments in neural networks, machine translation has become quite efficient at converting text from one language to another.

Translation Technology - NLP

Social Media Monitoring: Analyzing Customer Conversations

social media conversation analysis copy

More and more people are using social media and creating conversational data to share their thoughts in real-time on products, services, or topics. This feedback contains a lot of useful information about the likes and dislikes of individuals. Therefore, it is undoubtedly advantageous for brands to analyze this unstructured data and generate valuable insights. Thus, this is where Natural Language Processing takes the stage. 

These days, companies use various NLP techniques such as topic modelling to analyze social media posts and learn what customers think about their products. ​​From entities in the text to supervised learning, analyzing customer feedback and customer complaints with text analysis softwares can help you increase your customer interactions. Whether you can use it for your customer support team or your CX team, the customer experience journey must be understood and analyzed with social media mentions of your brand. You can take a look at Kimola Analytics to do Social Media Monitoring and Kimola Cognitive to analyze your unstructured text data. You can also visit our article on how to analyze social media conversations with machine learning and AI to see Kimola in action.

 

Chatbots

chat-bot

It would not be wrong to suggest that Customer Service is one of the essential departments of any company. A dedicated Customer Service process can help companies improve their products and increase customer satisfaction. However, manually interacting with each customer can be a tedious task, and that's where chatbots come into play to help companies achieve the goal of a seamless customer experience. Many companies use chatbots for their apps and websites to solve a customer's fundamental problems. Chatbots not only simplify the process for companies but also save customers from the frustration of waiting to interact with customer service.

Recruitment

The Human Resources department is an integral part of every company. They are responsible for selecting the right people for a company, which is one of the most critical functions in a business. But, in today's highly competitive world, HR professionals are swamped; they must review hundreds or sometimes thousands of resumes for a single position. Filtering resumes and shortlisting candidates can take hours. So, can't this job be automated? Of course, it can be! With the help of NLP applications such as Named Entity Recognition (NER), recruiters can find the right candidate easier than ever before. NER makes extracting information such as skills, names, locations, and education extremely simple and fast. 

Voice Assistants

Indeed, most of us have used Siri, Alexa, or Google Assistant. These Voice Assistants use Speech Recognition, Natural Language Understanding (NLU), and Natural Language Processing (NLP) to comprehend the user's verbal commands and perform actions accordingly. They might remind you of chatbots, but voice assistants are much more than that. A voice Assistant's tasks are much more flexible than a chatbot's. They can do just about anything—from remembering your daily habits to setting your morning alarm to finding restaurants according to your preferred cuisine and location, and even making grocery lists according to the recipes you pick. 

Grammar Checker

Correcting grammatical errors is another common application of NLP. These tools can correct grammar and spelling, offer preferable synonyms, and help present content more clearly and interactively. They also help improve the readability of the text, allowing you to convey your message in more compelling ways. Grammar checking tools like Grammarly can turn any ordinary writing into a captivating work of literature by providing tons of features that help a person write better content. 

However, if you took a look at the state of grammar checkers five years ago, you will see that they were not as capable as they are today. Do you know why? Because NLP applications were not as advanced as they currently are!

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Email Filtering

We all use email in our professional and everyday life, and you must have already noticed that when emails arrive, they are divided into categories such as primary, social, and promotions, as well as the infamous spam folder. Here, NLP uses a technique called Text Classification to filter emails. This technique refers to the process of classifying a piece of text according to predefined categories.

So, How Do We at Kimola, Benefit From Our Text Analytics / NLP Technology?

​​Kimola is a data analytics company that offers crucial insights by analyzing customer data in the form of unstructured text, which is not manageable by humans. It is nearly impossible to manually categorize and extract key information from such vast amounts of customer data without text analysis techniques and wastes time and effort. 

Text Analysis with Entity Recognition

Text Analysis - NLP

 

Machine Learning and NLP—specifically Named Entity Recognition (NER)—come to our rescue to help us derive invaluable knowledge from categorized data. This way, customer feedback is classified by Machine Learning technologies and classified into entities (location, organization, person, etc.) determined by Kimola, making it easier to gain crucial customer insights. Moreover, Kimola's Cognitive product allows you to use machine learning technology without any prior coding knowledge. Read our article on how to analyze unstructured text data without coding.

Free Pre-Built machine learning models to use with any plan at Kimola Cognitive.

free pre-built machine learning models
 Choose one of the pre-built machine learning models from Kimola Cognitive Gallery to classify and analyze your content. The models in the gallery support 6 languages and it has 10+ models (and counting!) to analyze consumer reviews in various sectors. Measure sentiment of your consumer reviews or any text data with Kimola’s ready-to-use Sentiment Model. See our gallery here.

Create Custom Machine Learning Models with Drag & Drop

create custom machine learning models with Kimola Cognitive

You can create your Custom Machine Learning Models to classify your data without having any programming skills. Just drag & drop your labeled excel file to train the AI and store your model to classify upcoming content. Tracking how consumer opinions are shifting by using the same custom model every month will help you to understand the trends of your consumers. (Are they discussing pricing all the time? Maybe it’s time for a discount!) Read our article on how to create custom machine learning models with easy drag & drop at Kimola Cognitive.

Find Free Datasets & Free Training Sets on our Github.

free cleaned datasets, training sets on github

Team Kimola knows how crucial it is to have clean datasets, so cleaned, ready datasets are reachable at Kimola's Github profile if dataholics want to work on them and do some magic. Check our Github profile here.

NLP in ResTech

NLP is also a very essential part of text analytics companies as enterprise level brands find customer feedback analysis very useful for CX teams. You can check our article on ResTech landscape to see the text analytics companies along with Kimola.

 

At the end of the day, as seen from the given examples, Natural Language Processing has become an inextricable part of our daily lives, even if we don't realize it most of the time. An increase in the number of these applications is inevitable, considering the rapid developments in the digital world and the new adaptations of NLP into different industries and business processes. While the human touch will remain essential for more complex communications in the foreseeable future, NLP will continue to increase its impact on our lives.

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