What Is Natural Language Processing?

Language is currency, according to James Cuthbertson, CRO of language analysis vendor Relative Insight in a recent episode of TechnologyAdvice’s B2B Nation podcast. Michael Pastore spoke with Cuthbertson about the applications of natural language processing (NLP) and more and why it’s important.

Increasingly customer-oriented marketing approaches are perhaps one of the major driving forces behind the growing importance of language analytics. NLP can foster a deeper, empathetic understanding for prospective and current clients.

Read more: 11 Ways to Be More Human in the Age of AI

What Is Natural Language Processing (NLP)?

NLP, also known as language analytics, is an emerging artificial intelligence (AI) technology that harnesses business insights from unstructured data that is qualitative in nature. An example might be customer responses in a sales call or on a social media outlet.

NLP takes human error out of analyzing language from large unstructured data sets.

As a type of AI, NLP understands oral or written texts much like humans do and takes human error out of analyzing language from large unstructured data sets. Language analytics tools can summarize, detect, and extract language from written text. They can also recognize oral speech to facilitate transcription and translation.

Why Is NLP becoming more popular?

As marketing strategies become more customer-centric, the “voice of the customer” (VoC) takes center stage for acquiring new customers and getting feedback from current customers. Natural language processing is one of the avenues through which to listen and gain business insights from customer voice.

Applications of NLP

One of the earliest applications of NLP was text translation. Here are a few key areas in which you can apply language analytics in your business:

  • brand positioning
  • the customer experience journey
  • internal HR communications

Language analytics informs your company’s brand positioning. Using this technology, you can see if your company is seen and recognized the way it wants to be and recalibrate your branding strategy if necessary.

NLP uncovers valuable insights into the customer experience in the sales journey, before and after sale. “Sentimental Analysis” is a subfield of this application whereby NLP tools can gauge emotions (even irony and sarcasm!) in human language.

So if you’re losing prospective and current customers, language analytics tools can help you understand and address their experience with your product or service. Language analytics can also be used internally to keep your finger on the pulse of company culture and the degree of employee satisfaction.

Read more: AI Equity in Business Technology: An Interview With Marshall Choy of SambaNova Systems

NLP Vendors in This Emerging Landscape

There are several NLP vendors on the market today, but here is a quick overview of just a few of them:

IBM’s Watson tools use NLP for text mining, text pattern discovery, sentimental analysis, customer interaction in the form of chatbots, and much more.

Relative Insight is a language analysis vendor that offers language analytics tools for marketing, sales, and HR. You simply upload your unstructured data into its platform and watch the insights roll in.

SAS Viya offers a suite of business intelligence tools that can, among other things, analyze unstructured data. In fact, 4 out of 9 of its tools include NLP.

What Is the future of NLP?

As the language analytics landscape continues to evolve, watch out for a few key trends:

  • Data Annotation: A related but likewise growing trend will be data annotation tools that clean unstructured data to make it more legible for ML tools, such as those used for NLP.
  • Ethics: Ethics in this type of AI will become increasingly important in terms of user privacy and data security. NLP tool vendors, especially those whose services target the healthcare industry, will need to be more transparent about what they do with the text data that their tools collect and how they store/maintain that data.
  • Digital Inclusivity: NLP tools will need to become more robust in recognizing language from BIPOC and non-native English speakers and writers. Moreover, given that most of this technology is developed in the United States, multilingual developers will likely be in high demand.

Read next: What Is Fully Homomorphic Encryption (FHE)?

Lauren Hansen
Lauren Hansen
Lauren Hansen is a writer for TechnologyAdvice, covering IT strategy and trends, enterprise networking, and PM software for CIOInsight.com, enterprisenetworkingplanet.com, project-management.com, and technologyadvice.com. When she's not writing about technology trends, she's working out or spending time with family.

Get the Free Newsletter!

Subscribe to Daily Tech Insider for top news, trends, and analysis.

Latest Articles