NLP is being used to breakdown human language in an attempt to find greater insights. When you load up a voice recognition application like Siri, NLP is being used to interpret everything you say into the microphone. As these programs become more sophisticated they will become better able to tackle the nuance of human language. In practice this means fewer misunderstandings and a seamless user experience.

The use cases are so vast that the NLP market is anticipated to be worth $13.4 billion by 2020. Human communication has become an incredibly valuable commodity for modern enterprises. Everything from our preferences to our opinions can be used to help advertisers product more effective advertising and targeted services.

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How modern enterprises are Using NLP sentiment analysis

Of all the applications of NLP there is one that outshines all others; sentiment analysis. Sentiment analysis or opinion mining is used to find and extract opinions from written text on everything from social media to blogs and forums. A sentiment analysis program is designed to identify the subject of the opinion, the person giving the opinion and whether the opinion expressed is positive or negative.

What makes sentiment analysis viable is that it can translate the unstructured opinions of consumers into transparent insights on products or services. Decision makers can then use this data to develop a more in depth understanding of their target audience. Nowhere is this more apparent than the financial industry where NLP is used for general sentiment analysis and for chatbots.

See also: The history of the chatbot: Where it was and where it’s going – Chatbots are not new. The software has been around for decades, and now its application is beginning to impact the enterprise — both internally and outward facing

Monitoring the market: sentiment analysis and modern financial institutions

Sentiment analysis has an innate appeal to financial institutions because it provides a means to anticipate how the market is moving. AI is used by many financial institutions such as JP Morgan in an attempt to improve trading, fund management and risk control strategies.

With NLP financial institutions can monitor the direction of a stock and keep tabs on public speculation. When the value of assets is so dependent on public opinion it can be very difficult to stay on the right side of the market. By analysing natural language, online banks and other institutions can keep tabs on public perception.

Most of the AI platforms using sentiment analysis are designed to quantify news sources such as blogs, articles and social media posts to asset the market. Sentifi’s Sentifi Maven is an example of a platform that uses sentiment analysis to collect news from over 13 million sources to see what topics are getting the most attention.

Related: Top five business analytics intelligence trends – Natural language processing (NLP) helps computers understand the meaning of human language.

Chatbots and the financial industry

Sentiment analysis is also being used in the financial industry within chat bots. Customers can communicate with chatbots to receive real-time updates, answers to questions and messages if fraudulent activity is detected. One of the most well-known chatbots platforms in the financial industry has been designed by Kasisto.

Kasisto delivers Kasisto Kai, a chatbot which customers can communicate with on Facebook Messenger, SMS and Slack. With Kasisto Kai customers can make payments, view account balance, check credit or loan applications and search for transactions.

Chatbots function well within the finance industry because they allow organisations to automate routine customer service activity. Rather than paying a representative to answer questions live, a bank can invest in a chatbot to manage lower priority support tasks.

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What is holding NLP back? (The limitations of natural language processing platforms)

Despite the fact that all this data is out there for the taking, AI platforms aren’t at the level where they can extract this information completely. Reading and interpreting human language is a hard task for a non-sentient machine. Concepts like irony and metaphors that come second nature to us are lost on computers.

Computers have a tendency to ignore the subtle nuances in favor of black and white interpretations. Misinterpretations are a common complaint of virtual assistants which are notorious for taking information at face value because they lack the ability to read between the lines.

Despite the limitations of NLP, AI platforms are improving every day. The more they are used the more they learn the more they are tweaked. Text summarisation, deep learning and semantic search offer companies from all sectors lots of opportunities in the near future.

Natural language processing goes digital

Now that the digital age is in full swing, there are an abundance of natural language processing communications up for grabs. While AI tools only have a limited capacity to leverage this data right now they are becoming more advanced every year. The barrier between human communication and the interpretative capabilities of AI is slowly and systematically being erased.

Over the next few years NLP will have a central role in developing chatbots and voice assistants. Once AI becomes more developed these tools will move beyond competency and into the realm of fluency with human communications. The removal of barriers to natural language will be one of the most disruptive influences in the technological world.