Natural Language Processing, or NLP modeling, is the hottest buzzword in science, technology, and the business world today. In laymen’s terminology, NLP modeling is a branch or derivative of artificial intelligence that helps computers or an intelligent system communicate as humans do. IT companies and scientists are digging deeper into NLP models and trying to carve out processes from them to make lives easier and more interesting. Every business wants to imbibe NLP modeling into its framework or environment somehow. NLP tools are eminent for business success because they deal with massive amounts of unstructured data in the form of texts, files, emails, social media chats, survey results, and much more.
We are surrounded by various applications of NLP modeling without even realizing what they are. They have become so integral to our personal and professional lives that we cannot exempt them from our systems easily. Applications of NLP modeling in our work and personal lives can help us analyze data and make data-driven smart decisions. These also help us automate tasks and improve major business processes. We will now highlight the top five applications of NLP modeling.
Emotion Detection and Analysis
NLP modeling is very effective, especially when it comes to performing sentiment analysis or detecting emotions that machines are not capable of. Sentiment analysis or communicating feelings to a machine is difficult in normal circumstances. Machines cannot comprehend sarcasm, irony, or other subtle nuances in emotions. Sentiment analysis refers to the ability to recognize emotions, opinions, and feeling and their degree or severity. NLP modeling enables sentiment analysis in real-time by gauging customer reactions. It would be very useful for monitoring customer feedback regarding a company’s products, services, new launches, or marketing campaigns. Through NLP modeling, a computer could detect how a customer feels about the company and its offers and what is the severity of their emotion.
Have you ever wondered how a mobile app or messaging software checks your grammar, spelling, and structure whenever you type? How does a mobile auto-correct your mistakes and auto-predicts the text you are about to write? This is yet another fascinating application of NLP modeling, which allows software and computer functions to suggest words and correct embarrassing mistakes. NLP modeling is the magic behind suggesting synonyms or texts, correcting grammar, rectifying spelling mistakes, rephrasing sentences, and even predicting the tone of the sentence. It saves the time and effort of writing long scripts. Using NLP modeling, computers are behaving like humans and even better than humans in detecting and correcting mistakes that human-eye could have missed easily.
Text or automatic summarization is a pretty useful tool, especially for writers or freelancers. This is another innovative and useful application of NLP modeling, where a computer summarizes the text itself. Machine and Artificial Intelligence services can extract important information while leaving behind redundant and extra words to simplify the process. Thus, the Natural language process parses through huge amounts of data and extracts useful information from it to make the job easier. The natural language process summarizes data through two approaches:
- Extraction-based summarization: It extracts keywords, phrases, and important information to create summary
- Abstraction-based summarization: It forms new phrases and sentences from the original data source.
Market Intelligence Reporting
Marketers and businesses benefit from this application of NLP, where they get to learn about their customers and use this information for their new products and releases. It helps them to create customer-centric marketing strategies. Market intelligence reporting helps to detect customer pain points, needs, and complaints into consideration. It also gives an insight into competitors’ activities and moves to grab opportunities and their weaknesses.
This NLP technique is very useful in determining the urgency and priority of your text. Through the urgency detection model, you can set a criterion for preference, priority, and emergency so the system recognizes the words and expressions that require immediate attention. It also detects the gravity of the matter and helps prioritize urgent and important requests. Urgency detection prevents piles of unresolved requests and tickets in the first place. It aims to improve response time, efficiency, customer service, customer satisfaction, and system productivity.
Out of all these applications, and IT support which one have you used and found very useful?