From being valued at USD 10.93 billion in 2019 to USD 34.80 billion by 2025, the natural language processing market will rule the AI job markets by storm. During the past years, algorithms and deep learning architects made quite a significant impact in the analytics realm.

Nonetheless, multiple organizations involved in marketing have started adopting text analytics solutions to boost their marketing programs. Thanks to the rising trend for mobile marketing.

Natural language processing (NLP): definition

NLP, a subfield of AI that allows computers to understand the human’s language. The major ultimatum for the NLP is to read, comprehend, understand, and derive useful information from the human language.

As humans, we communicate by reading and writing in different languages. However, it is not the same with computers – they only understand via its native language i.e. machine language or machine code which humans cannot understand.

70 years ago, programmers used punch cards to communicate with the very first computers and this process was easily understood by a relatively small number of people. Today, for instance, you can tell Alexa you like the song that is playing, and in response, it lowers the volume and says Ok, in a human-like voice. This then adapts its algorithms to play this song the next time you listen to this music station.

How did your device work? Well, the device got activated as soon as it heard you speak those words, it understood the unspoken intent in your comment, executed the action, then gave its feedback in a well-formed English sentence – all of this happened within five seconds. This could be made possible only with the help of NLP, deep learning and machine learning.

Significance of NLP

Truth be told. Billions of texts are generated daily via apps such as WhatsApp, WeChat, Telegram or through social media channels like Facebook, Twitter, and Instagram, forums such as Reddit and Quora and via blogs, google searches, and news publishing platform. These channels are constantly producing huge amounts of data within seconds. Owing to this, we can no longer use the old and traditional method of understanding this data, this is where NLP comes to play.

Helps structure highly unstructured data

Humans express themselves in infinite ways such as writing or speaking verbally. It is complex in this manner. Overall, there are more than hundreds of languages in the world, each having a unique set of grammar, syntax, and slang. Moreover, when we’re writing, we often tend to misspell words or miss punctuations. And when we speak, we have different regional dialects and accents. At times, we stammer and stutter while learning terms from other languages.

NLP helps resolve ambiguity in such languages thus adding useful numeric structure to the data that will further help you via applications such as text analytics and speech recognition.

Communicating with humans made easy

With the help of NLP, computers can now easily communicate with humans in their language. For instance, NLP can now easily make the computer read a text, listen to a speech, and make an interpretation out of it, and finally come out with a conclusion stating which part of the communication is relevant and important.

Used cases of NLP and its applications

Even a simple NLP algorithm is based on a machine-learning algorithm. With the help of machine learning, large sets of rules do not require hand coding anymore. Machine learning can automatically learn these set of rules via analyzing certain examples like learning it through a large corpus and finally making a statistical inference.

Simply said, the more data the machine can analyze, the more accurate your model will turn out to be.

1) Sentiment analysis

One of the most commonly used cases of NLP. With the help of sentiment analysis, data scientists can easily assess comments found on social media webpages to analyze how their business has performed. For instance, a review note from a customer could tell you the areas where people expect you to perform better.

2) Automating processes in customer services

This concept reduces the time taken in customer support, saving the executives valuable time and help in making processes even more efficient.

Example, Uber designed their ticket routing workflow, that includes tagging via country, type (lost item, a question about payment, and driver-partner), along with the language. However, this workflow has been prioritized with certain rules.

Consider a service like this, parameters such as severity, urgency, and queries for all your complex problems can easily be solved.

3) Chatbots

Whenever you open a travel website or an education platform, you will notice a pop-up willing to assist you with your queries. Since people could not always be behind the backend handling all your queries, chatbots were invented. They’re simple computer programs that simulate a human conversation. With the help of NLP, the chatbot can understand the intent of the sentence, keywords, relevant topics they’re looking for an answer, and at times even emotions, based on which they analyze and then come up with the best response possible.

Though NLP is still at its infancy as compared to other data science technologies like neural networks and deep learning, it has piqued quite a buzz in the global community.