Natural language processing is a branch of artificial intelligence concerned with the interactions between computer and human languages. Put simply, it’s about the ability of computer software to “listen to,” process, and understand human language in the way that it is spoken.
In 2019, almost all of us are familiar with natural language processing: It’s what makes it possible for voice-activated assistants like Siri or Alexa to understand spoken requests and then answer them. However, natural language processing does refer to the interpretation of both speech and text — basically, anything said or written to a computer that isn’t in a programming language.
Current approaches are “based on deep learning, a type of AI that examines and uses patterns in data to improve a program’s understanding” of language over time, as Margaret Rouse states in Search Engine Analytics. “Deep learning models require massive amounts of labeled data to train on and identify relevant correlations, and assembling this kind of big data set is one of the main hurdles to NLP currently.”
But as natural language processing improves, so does the ability for seamless understanding between people and their devices — and the removal of this friction opens up new opportunities for marketers.
Why does natural language processing matter to marketers?
Natural language processing already affects marketers business from two angles: First, because of the growth in voice-based queries over the past several years, and second, because of the growing importance of understanding the intent behind users queries when it comes to search marketing.
While understanding queries has always mattered to marketers, the new search paradigm — in which your customers look for structured answers to their questions — prizes understanding the intent behind a users query. This means that marketers need to get better at understanding intent as expressed via questions, and AI is a compelling way to do this.
It also means there’s a need to get smarter about finding out what your customers are saying online. One of the most promising applications of natural language processing for marketers at present is in the realm of sentiment analysis — the process of identifying the opinions or feelings expressed in a piece of text or speech. The goal is to determine if the attitude toward your product or business is positive, negative, or neutral.
Gathering data about how a person feels towards your brand (i.e., what words they used in a tweet, how they asked Siri for your customer service number) can help you understand your customers, respond to them more effectively, and provide a better experience. It enables you to stay one step ahead when providing happy customers with the right experiences — as well as potentially even anticipating negative reviews. Sentiment analysis helps you take your understanding of what your customers are saying online to the next level.
With the right tools, leveraging natural language processing for sentiment analysis allows you to analyze customer feedback at scale, so you better understand your customers and how their attitudes may impact your business ratings and reviews.