Unlocking the Potential of Natural Language Processing

With the help of artificial intelligence, natural language processing (NLP) and human expertise, it’s becoming easier than ever for people to connect with machines. NLP allows machines to understand the nuances of human language, allowing humans to communicate with them through text and speech.

Machine translation, speech recognition and text data analysis can be a bit tricky to understand and react to. But a computer’s ability to interpret and process the nuances of human language is critical to creating successful software programs that can be used by both companies and consumers.

NLP is the art of combining artificial intelligence, machine learning and linguistics to understand, analyze and interpret human language. It can be used to improve search engine results, automate customer service tasks, personalize user experiences, generate insights from unstructured data and more.

There are three main aspects to NLP: speech-to-text, natural language understanding and natural language synthesis. In the first part, computers convert speech into a programming language by using a statistical model. They then break the speech into tiny units and compare them to previous units from previous speeches. This information is then analyzed and interpreted to determine the resulting words and sentences.

The second part is natural language understanding, where the computer uses a collection of rules to interpret and manipulate the natural language that it receives. These rules can be based on a number of things, including grammar, word order, context and even the specific meaning of the text.

This enables the computer to read and comprehend human texts, as well as transcribe them. This can be accomplished through a variety of methods, including recurrent neural networks, hidden Markov models, dynamic programming and word embeddings.

Natural language understanding can be used to perform text mining, sentiment analysis and predictive analytics. Those methods can be applied to a wide range of applications, from healthcare to cybersecurity.

Sentiment Analysis

A major use case for NLP is sentiment analysis, which helps businesses understand what their customers are thinking and feeling about them. Sentiment can be derived from a wide range of sources, including social media, comments in call centers and even customer feedback on surveys.

Business Management

In many organizations, business managers rely on small teams of experts to collect and aggregate data in order to extract useful insights for their business. However, due to the volume of data that needs to be analyzed in order to extract meaningful business intelligence, this is usually a time-consuming and tedious task.

With the help of NLP, business managers can use tools like Qualtrics Experience iD and XM Discover to transform the vast amounts of customer service data they have into actionable insight, in a fraction of the time that it would take a human. This can result in the automation of a variety of time-consuming and labor-intensive tasks, freeing up business leaders to do what they do best – run their businesses.

NLP also enables businesses to turn a huge influx of customer feedback into actionable insight, delivering real-time customer satisfaction and enhancing overall business performance. This is especially important for companies with highly competitive markets where the ability to quickly and effectively understand what customers are saying about them across all touchpoints can make the difference between success and failure.

Unlocking the Potential of Natural Language Processing

In a world where 95% of customer data is found in unstructured text (emails, surveys, Twitter posts, and more), companies need to take advantage of the vast opportunities offered by natural language processing. It is a powerful tool that enables businesses to automate their processes, generate insights, and streamline their workflows.

Chatbots for customer support:

One of the most popular applications of NLP is a chatbot, which is used to answer questions and troubleshoot issues in online business chat. These bots are able to learn from thousands of conversations, and the combination of big data and machine learning allows them to respond in a manner that mimics a human.

Sentiment analysis:

Another common NLP application is sentiment analysis. This combines text mining with machine learning to analyze the language of a large group of social media users and identify their responses in a way that can help a brand understand the audience's reactions. This information can then be applied to create new ads, products, or campaigns that resonate with consumers.

E-commerce search:

As the internet has become a primary way to conduct online purchases, companies have become increasingly interested in leveraging NLP to make searches easier. NLP helps search engines proactively understand a searcher's intent and provide relevant results faster. This enables companies to increase revenue per visit and average order value through delivering personalized content and presenting the most relevant items on an e-commerce website.

Insurance fraud detection:

Another important application of NLP is in identifying fraudulent claims and flagging them for further investigation. This allows insurers to monitor their ultra-competitive market landscape and spot patterns in claims that could indicate fraud before it becomes an issue.


Currently, NLP is helping researchers battle the COVID-19 pandemic by analyzing incoming emails and live chat data from patient help lines to identify those at risk of contracting the virus. This technology has also enabled physicians to proactively prioritize patients and get them into the hospital quicker.

Health data management:

NLP also enables physicians to easily summarize and log lengthy medical information, utilizing their notes and clinical findings faster and more accurately. This enables them to improve diagnosis, treatments, and ultimately patient outcomes.

Smart assistants:

Artificial intelligence and machine learning technologies have created a number of smart devices, including virtual assistants like Siri and Alexa. These apps are able to use NLP to understand speech and tone and deliver helpful and intelligent answers to questions.

Search results:

A lot of the time, search engine users expect a particular word or phrase to appear on their searches, so it is critical for them to be able to find the right information quickly and easily. This can only be achieved by using NLP to proactively understand what a user is searching for and delivering relevant results.