Applications of Natural Language Processing in Businesses

Recent advances in artificial intelligence and machine learning have enabled innovative approaches and advances in the field of natural language processing. In this article we will discuss Natural Language Processing and how businesses are profiting off it.

What is Natural Language Processing?

Natural language processing (NLP) is a subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to process and analyze large amounts of natural language data.

Computer programs usually require human instructions fed to them in a language they can understand to deliver desired outcomes. Natural Language Processing is the ability for programs to understand and interpret instructions as fed to them in human language.

How does Natural Language Processing work?

Natural Language Processing techniques are based on simple Supervised Machine Learning Algorithms that are trained to identify certain words and phrases in a sentence and then respond with fixed sentences. These techniques were popular for their use in chatbots on different websites.

Newer techniques use advanced Deep Learning tools for more evolved human-like experience. A large set of labeled data is used for training advanced deep learning models to understand sentence breaking, grammar, semantics and many other features.

These models are then used to develop software and tools that understand and respond to human voice and text instructions.

Business Applications of Natural Language Processing

Natural Language Processing has found use cases in a number of industries and has augmented profits for a lot of businesses in various ways:

1. Sentiment Analysis

With the amount of data available, manual analysis of data has become practically impossible.

For a lot of businesses, usually online businesses, reviews and sentiments are expressed by consumers on various platforms like the business' website, social media, etc. and they need to be addressed by business owners for marketing and management.

There is no correct or structured way to give feedback so users write their reviews in a lot of different ways. So, resolving the issues faced by customers becomes a problem as reading all the reviews becomes impossible.

Sentiment Analysis is a technique using Natural Language Processing to define 'polarity' in sentences such as good or bad, or happy or unhappy and proceeds with suitable responses based on it.

2. Voice Recognition 

Voice and speech recognition helps programs convert speech-to-text to perform certain tasks.

Everyday examples of voice and speech recognition include Google Assistant, Siri and Alexa.

Voice recognition systems are pretty efficient now-a-days. Google Assistant claims over 95% accuracy.

With the integration of these systems with speakers this technology has become a part of our everyday life. Around 65% of 24-49-year-old people talk to their voice assistants at least once a day.

3. Chatbots

Chatbots have found a way in our lives without us even realizing it. Gartner predicts that by the end of the year 2020, an average human will have more interactions with a chatbot than with their own spouse.

Chatbots have helped businesses exponentially reduce their turnaround time for consumer complaints.

NLP based tools help chatbots with breaking down sentences and semantically understand the inputs made by the consumers. The chatbots are fed with standard responses and allowed to interact with customers depending on their queries.

90% of businesses have reported that chatbots provide a positive resolution to their customer grievance handling system.

4. Advertising and Content generation

Natural Language Processing has overtaken some aspects of advertising as well. Alibaba, in 2018, introduced AI Copywriter for advertising businesses to help them create copies for product descriptions, slogans and other adverts.

AI Copywriter is a Natural Language Processing based tool that is capable of generating 20,000 words per second.

Such Natural Language Processing tools recognize the interests and intents of the audience through their written words and help target them more effectively.

Natural Language Processing also helps in effective ad placements. Natural Language Processing helps identify relevant pieces of content for ads. For example, it is more suitable to advertise games on a Gaming YouTube video than furniture.

5. Market Intelligence

There is an overwhelming amount of content online. Sifting through millions of blogs, articles, news pieces, social media posts and e-mails is not possible.

Natural Language Processing uses summarization techniques and triggers to prioritize and discard these pieces of content.

Businesses have to be updated with the trends in the industry, changes in policies, competitor's performance and how people are perceiving their competitors and industry.

Natural Language Processing helps businesses keep track of the market.

Conclusion

These are some of the ways the businesses are using Natural Language Processing techniques to grow. Businesses will continue seeking newer ways in which Natural Language Processing and in general Machine Learning and Deep Learning can improve their business processes.