Are you sure your business is harnessing the power of modern technologies as it ideally should? Things like Artificial Intelligence, Machine Learning, Natural Language Processing, and Data Science can immensely improve every business area.
While these scientific disciplines offer advanced opportunities to smooth your business operations, this article will help you spot the errors that Natural Language Processing can remove.
Definition of Natural Language Processing
NLP stands for Natural Language Processing. If you have a tech background, chances are you familiar with this term. Surprisingly even if you are not, you are still using it daily, wherever on the earth you reside. Yes, a lot of folks across the globe interact with NLP without realizing it.
In short, NLP is the science of using machine learning to derive meaning from human languages. This cutting-edge technology has been a part of our lives for years now.
Why Should I Care?
The purpose of this article is to help businesses realize NLP opportunities in their business problems. In the light of shared examples, you can think about how NLP can make your product or service smarter. If your job is planning or building products, there is a high probability that Natural Language Processing has too much to offer you. A fluent conversation about this with your managers is a must.
Do I Need Deep Technical Knowledge of NLP?
You do not need it at all. However, what intimidates leaders and product managers is a lot of jargon and technical details. Think of it as a toolkit whose working mechanism is not something you should necessarily learn. But you should understand very well how to use it.
You do not need to know the mechanics of your vehicle’s engine, but you should know that it is there to help you commute. Can you identify your business areas with the gaps that NLP is capable of filling? If yes, you have enough knowledge of Natural Language Processing.
Examples of NLP helping Businesses
Now let us dive into the real subject of this article. Reading these examples may leave you amazed about the versatility of Natural Language Processing.
- Social Media Monitoring
If you are a content marketer, you must have an idea of social media monitoring and its importance. This area deserves to be on the top of the list. Popular social media monitoring tools like Buffer or Hootsuite work with NLP technology. They help you maintain the social media channels of your brand. When people are talking about your brand, their job is to let you know through an alert.
One negative review is enough to destroy a brand’s reputation. Marketers, business owners, and content marketers understand the sensitivity of such a situation. Hence, social media monitoring is more than necessary to avoid such a crisis.
- Sentiment Analysis
This second example is a subset of social media monitoring. The sentiment analysis is all about identifying opinions and determining the intent of the author. It could be a positive, negative, or neutral opinion towards the brand. NLP helps pick out the emotionally charged words. The words that describe the customer’s experience with the product or a brand are emotionally charged words.
If the post is full of positive words like amazing, fantastic, wonderful, then NLP will tell the software that the overall intent is positive. So with the help of this NLP operation, businesses can easily estimate how receptive their targeted customers are. That covers the responsiveness in two aspects. It could be in general or about any recent change.
Content marketers whose jobs are to administer social media pages of brands are well aware of all this stuff. Things like Sentiment analyses and Social Media Monitoring make their jobs easy.
- Text Analysis
If you are a writer, text analysis is perhaps what you do all day. The process is breakable into several sub-categories. Some examples of this are morphological, grammatical, syntactic, and semantic analysis. Example programs that perform text analysis are Grammarly, Yoast SEO, and Hemmingway App, etc. These names cannot be new for you if you are a writer.
Text analysis works on extracting different fundamental elements from the text. Some of the examples are topics, people, dates, locations, companies, etc. Writers can improve the quality of their work significantly.
Companies can organize their data and identify useful patterns and insights. It helps e-commerce companies to figure out the likes and dislikes of the audience by analyzing their reviews. When reviews are hundreds, manual analysis is near to impossible. NLP equipped tools such as Wonderflow’s Wonderboard are used for the purpose.
- Survey Analytics
Product review is not the only thing to analyze. Companies may need the analysis of their survey results to get actionable information. NLP makes the raw data useful and workable. Again, when you are surveying the entire database of 10,000 customers, you can’t go through all of them yourself.
- Spam Filters
Statistics claim that 45% of all mail sent is spam. Then how come you and I don’t receive our share from this terrific amount. What saves us from this headache? Well, they are excellent spam filters, backed by Natural Language Processing. They carefully inspect email subject lines and body content. Perhaps, this is the best example of an average human being enjoying NLP wonders without knowing it.
- Email Classification
Gmail inbox shows you three tabs of categorized emails, which are primary, social, and promotions. Personal emails are in the primary tab. The notifications from social media platforms go in the social one. Promotions tab is where the newsletters of subscribed websites end up. All this would not have been possible were it not for NLP.
- Spelling Check
Both students and professionals heavily rely on this feature for daily tasks. Being humans, we tend to forget spellings of different words, but this is what machines do good.
Can you submit your university assignment without proofreading for spelling? Can you submit an email to the CEO of your company without this? Both are impossible. Spell check is effective at flagging out spelling and grammatical mistakes. The manual procedure of this would not be less than a nightmare when documents have thousands of words. The tools like Grammarly also use NLP to facilitate their users.
- Autocomplete
We all go through this every day. Based on the initial few characters, Google predicts our best interest. Amazingly, it is true most of the time.
SEO professionals work on different keywords to rank their or their client’s websites. Google utilizes a trove of data that enlists what other consumers are typing to find a specific thing. It helps us find relevant keywords. To understand the difference between different keywords or terms, NLP is crucial.
- Autocorrect
The beauty of autocorrect could be asked from lazy people, or those with fat fingers, as they often mistakenly type the wrong letter. Writers also love this feature a lot.
To provide an autocorrect facility, NLP identifies the closest possible word to your mistyped word.
- Smart Search
Often, when we search a particular keyword on a website, it suggests some relevant results to help us find the desired thing quickly. Behind the scenes, this is not that simple.
Let’s picture a scenario for perfect elaboration. You are browsing an e-commerce website. Let’s say it is a culinary store. You want to buy a plastic glass, and start your search with plastics. Now, who will tell the search engine that plastic glass must come at the top of the results? Well, Natural Language Processing techniques governing the website will do this.
The store will pick up on the context and add the relevant synonyms to the search results.
- Translation Tools
Nothing but a translation app has your back in a foreign country, where people do not understand English.
So far, the most popular tool is Google Translate. Five hundred million people around the world use it every day to understand more than 100 languages. Intuitively, not only Google Translate, but all the translation programs in the world operate through natural language processing.
- Duplicate Detection
Often two different sentences convey the same meaning. The similar questions asked in other sentences would cause flash floods of data with no useful purpose.
Let’s consider this question. ‘What makes Machine Learning different from Artificial Intelligence?’ Now, you may ask it in a variety of ways.
Check a few of the examples here.
- ‘Are Artificial Intelligence and Machine learning the same?’
- ‘Is Machine Learning Different from Artificial Intelligence? ’
- ‘What makes Artificial Intelligence different from Machine Learning?’
- ‘Are Machine Learning and Artificial Intelligence the same? ‘
The answering websites like Quora use duplicated detection technology to keep their site functioning at its best. It collates all the questions to one, for time and energy saving.
When the user finishes typing the question at quora, the website checks if it is linguistically similar to any other query asked. If it finds any, it directs the user to their answers.
- Chatbots
You must have encountered a little window on the e-commerce websites at the down right corner of the screen. These chat agents answer your inquiries. How do they understand your questions and provide the correct answers? Again, it is the magic of NLP.
These Chatbots can decipher human questions and help them by suggesting the relevant products and booking meetings.
For up to the mark performance, companies are also working on advanced Chatbots these days. These upgraded Chatbots are more like virtual assistants. They use NLP to let the end-users know about their spending patterns and entitled promotions. A business that claims to be providing natural language processing services must include Chatbots in its services.
- Knowledge Bases
Stories of NLP’s magic do not finish here. Do you know about knowledge bases? They are a portal of online information that includes FAQs, troubleshooting guides, and much more.
Knowledge bases professionally simplify business operations. 24/7 customers can solve their problems, with little or no need to get in touch with the company and waiting for their response. The company’s best interest is to let the customers approach the right material as quickly as possible. That is why knowledge bases contain thousands of documents.
But what it has to with Natural Language Processing. Well, it is possible to connect the Chatbots to the knowledge base. Configure your bots to send the customer the right material, and increase your conversions to the maximum.
- Alexa and Google Home
The fame of these smart home devices is multiplying every passing day. Younger generations use them all the time. What makes them super convenient is their ability to multitask. Instruct Google Home to run your favorite music when you are way too tired to put it on yourself.
NLP is working behind the scenes when you issue a voice command to your smartphone assistant.
- Organized Medical Information
Usually, when patients visit clinics, they share their symptoms with nurse or counter staff. Then, those persons make notes to send to the physician. To streamline the patient data, clinics these days use Natural Language Processing techniques. That automates the process of understanding the patient information.
There is an NLP powered tool known as 98poin6. It allows the patients to share their health history and condition before the physical meeting. They send it through the app. The app then streamlines the relevant information and sends it to the physician.
- Aircraft Maintenance
This entry may seem like an oddball on the list. Companies use NLP to analyze the reports submitted by the pilot or the other staff. They can, therefore, improve their processes and systems.
- Digital Phone Calls
‘This call may be recording for training purposes.’ All of us have heard this phrase. Ever wondered what this is all about? Every time these recordings do not serve for training purposes. Sometimes they are sent to the NLP database for its learning and improvement.
- Targeted Advertising
Showing the latest mobile ads to 80-year-old people is most probably a wastage of money and time resources. People of that age group, in most cases, have no interest in new phones. But those born after 1980 are mostly looking for this kind of information.
Most companies like to use the Targeted Advertising approach. It saves a lot of money and shows the ads to only potential customers. It works on Keyword matching. The ads are associated with a keyword or a phrase. It only shows up in the feeds of users who search for similar keywords.
The Ball in Your Court
Natural Language Processing has numerous exciting applications. They provide insights that aid in business decision making and automates time-consuming tasks.
It is now your turn to figure out the areas where your business desperately requires some NLP operations. It is time to grow your business and find the best vendor for Natural Language Processing services.
