Natural Language Processing
7 Modern-Day Natural Language Processing Applications for Businesses
Here’s my take on some of the modern-day natural language processing applications for businesses.
Words, words, words….Have you ever wondered how significant they are? It is hard to imagine our world without communications, messages, books, songs, telephone conversations, movies, etc. Try to remember the number of text and voice data that you receive on Whatsapp daily.
Have you ever thought about deriving meaning from this data and use it in a meaningful way? The good news is, with the advancement of technology, it is now possible to perform additional functions with our language.
These systems are based on NLP — Natural Language Processing — the perfect combo of artificial intelligence and computational linguistics. Nowadays, it has become essential for businesses to utilize modern-day natural language processing applications.
But, before we discuss the modern-day natural language processing applications for business, let’s look at the definition of NLP.
What is Natural Language Processing?
If you have never come across NLP, simply open Google, click on access to voice match and say something, “Ok, Google….” You can try something similar on Siri from Apple, Cortona from Microsoft. Once you say something, with the help of NLP-based devices that decipher the human language, you will get the desired information based on your voice request.
In short, NLP is the machine’s ability to process the things that you say, structure the information that is received, ascertain the appropriate response, and respond in a language that you can decode.
The next question is, how does NLP work, and what is NLP used for?
Let me answer that question in the next section.
How Does Natural Language Processing Work?
Have you ever wondered what do words and phrases mean to a computer which can only decode zeroes and ones? It might not be an easy task to teach machines to comprehend our communication. Well, yes and no. In short, the process of machine understanding with the aid of natural language algorithms follows this process:
All in all, NLP is the process of creating algorithms that convert the text into words and then label them based on the position and function of words in the sentence. For this, it utilizes word embedding as a silver bullet to resolve several NLP problems. This way, it converts human language meaningfully into a numerical form. This enables computers to comprehend the nuances implicitly encoded into our languages.
The idea is to convert every word into a set of numbers — an N-dimensional vector that stores information containing the word’s meaning. Every word gets assigned an exclusive vector/embedding. However, similar words end up having values closer to each other. A prime example of that is, the vectors of the words “Man & Boy” would have a higher similarity than vectors for “Boy & Lion.”
This way, NLP serves two purposes. The first one is to enhance other NLP tasks like machine translation. The second one is to ascertain the similarities between words and groups of words. Of course, everything works out smoothly in case the task is simple and straightforward. However, the fantastic thing about the beauty of human speech is that human speech is significantly distinct from a robot’s speech. The main difficulty for the developers is that the machine takes the meaning of every word literally. And the human language is very saturated, consisting of poly-semantic words and hidden meanings.
This is where advancements in technology have led to businesses exploring these modern-day natural language processing applications to make their brands a huge success. As promised, let’s now look at these natural language processing applications in our next section.
7 Modern-Day Natural Language Processing Applications for Businesses
1. Sentiment Analysis
Apart from comprehending what people say, machines nowadays can decode the emotional context behind the words being spoken. This is popularly known as sentiment analysis. It is often used to measure customer opinions, monitor a company’s reputation, and generally understand whether the customers are happy with your product or service or not.
Today, sentiment analysis is used often. Different tools predict what people say about your brand on social media. This way, you can decode their opinion. This technology can be exceptionally perspective. Hence, it is considered to be one of the best modern-day natural language processing applications for businesses.
A prime example of that is, researchers at the Microsoft Research Labs at Washington were able to predict which women were at risk of postnatal depression by merely scrutinizing their Twitter posts. The impressive part is, the research was based on what women said in the weeks leading up to giving birth.
Some of the prominent companies employing Sentiment Analysis are Lexalytics and Monkey Learn.
2. Chatbot
Customer service automation provides excellent opportunities to put NLP to work. Chatbots built using NLP technologies provide a lucrative opportunity to remove humans from generic queries and ask them to concentrate on more critical customer problems. The eCommerce and customer support sectors have been utilizing them to great effects for several years now.
NLP-powered chatbots can aid in answering the queries of customers within seconds. It can also increase the conversions by making it easier for customers to ascertain what they want to buy and by changing lead generation, all of this while employing a conversational tone.
Here’s an example of the way Splashtop utilized Acquire chatbots to increase sales conversion by 35%. Here the NLP component processes the situation in its entirety instead of taking the customer’s verbatim input:
Some of the prominent companies employing NLP-enabled chatbots are Acquire, IntelliTicks, QuickReply.ai, Kore.AI, and Haptik.
3. Text Classification
Text Classification is a perfect way to label natural language texts into a predefined set of relevant categories. The ultimate objective of performing this exercise is to categorize the information from a large chunk of data. With the help of such modern-day natural language processing applications, it becomes possible to enhance the searchability of different systems.
Text Classification is a perfect way to label natural language texts into a predefined set of relevant categories. The ultimate objective of performing this exercise is to categorize the information from a large chunk of data. With the help of such modern-day natural language processing applications, it becomes possible to enhance the searchability of different systems.
Some text classification examples include tagging of contents on e-commerce, blogs, social media apps, and news apps.
Different organizations are employing this modern-day natural processing application to manage unstructured text. One company is Lionbridge.ai that predominantly concentrates on providing text classification services.
4. Market Intelligence
NLP can enhance market intelligence in different ways with the help of text analysis. Market intelligence comprises of market knowledge or information shared between governments, companies, and other regulatory bodies. The information that you obtain through market intelligence can aid companies in developing new strategies. This is where NLP can assist in extracting this information from raw business data.
With the help of NLP, you can track and monitor market intelligence reports. It can be in the form of sales and marketing data, brand reputation, and product data. NLP text analysis extracts critical insights from unstructured data assisting companies in making informed decisions and devising future strategies. It even aids in revealing patterns in scattered data, which can be used for further scrutinization.
We see natural language processing applications using market intelligence in Financial Marketing. The reason for that is, NLP provides comprehensive information like the status of the market tender delays and analyses the past earnings/annual reports to estimate future growth.
There are various benefits of natural language processing applications in market intelligence. It enhances data access and improves data quality, which aids businesses to save costs and improves their decision-making capabilities.
Skl.ai is a prime example of a power NLP platform that utilizes market intelligence by collecting information from unstructured text with the aid of sentiment analysis, entity extraction, entity analysis, and content classification.
5. Speech Recognition
We, humans, have been given a unique ability to express our thoughts in the external world with the help of our speech. Speech recognition is one of the ways through which machines are made capable of comprehending the human language.
Some of the top companies employing Speech Recognition as a part of natural language process applications for business are Nuance Communications, Google LLC, Amazon.com Inc., and Apple Inc.
Amazon’s Alexa, Apple’s Siri, and Google’s Voice Assistant are some of the reputed voice assistants that can decipher our voice commands for daily tasks in an effortless manner with the help of speech recognition technology using NLP.
Nuance is a reputed brand that has developed a virtual assistant known as Nina. It performs exceptionally well and assists in attracting more companies to avail of their services.
Take a closer look at how Nina — the smart virtual assistant, works in the YouTube video below.
6. Managing the Advertisement Funnel
Have you ever wondered what exactly does your customer really need? Where exactly is your customer trying to search for his/her needs? All these questions can be answered by using a natural language processing application to manage the advertisement funnel.
With the help of NLP, you can conduct intelligent targeting and placement of advertisements in the right place and at the right time. Reaching out to the right person at the right time for your product is the ultimate objective of any business. NLP matches the exact keywords in the text and assists in attracting the right target audience. Although keyword matching seems to be a minuscule task of NLP, it can be highly lucrative for businesses.
7. Customer Service
Every organization hopes to keep its customers happy to ensure their loyalty towards the brand. However, it is not that easy. With the help of natural language processing applications, especially for customer service, you can quickly gain insights into your target audience’s tastes, preferences, and perceptions. This is where the speech separation functionality of NLP comes into the picture, where the AI identifies each voice of the corresponding speaker and answers to each caller separately.
With the help of excellent text-to-speech systems, even the blinds can use the customer service efficiently. For example, with the help of the call recording feature, you can get valuable insights about a particular customer and find out whether he/she is happy or sad with your services. You can even adjudge their needs and future requirements.
With the help of NLP, it is possible to translate the caller’s speech into a text message which can easily be scrutinized by an engineer. All in all, it can be an effective way to learn the pulse of your audience.
A prime example of one company utilizing natural language processing applications for its customer service is Amazon. The company has claimed that 35% of its revenue comes from purchases customers found through recommendations. Earlier, keywords were the prime focus for product recommendations.
However, with time, retailers are adding contexts, previous search data, and other factors to enrich their product suggestions feature. Thus, with the help of NLP, retailers at Amazon get invaluable insights that aid them in making these combinations and get the recommendations on the spot.
Concluding Thoughts
In the last decade or so, the IT industry has taken its leap of faith and has dug in deep when using modern-day natural language processing applications for their businesses.
Businesses have experimented a lot and have started to utilize NLP effectively. Yet, with a lot of data and processes at disposal, it will still take some time for businesses to comprehend, scrutinize, and respond to the needs of the human mind.
