Top Natural Language Processing Applications in Business
One of the most innovative areas of Artificial Intelligence (AI) is Natural Language Processing (NLP). NLP enables computers to decode human language and use the information to make decisions. NLP enables computers to converse with people in their language, in other words.
Over the past few decades, NLP technologies have evolved significantly and become indispensable in our daily lives. I imagine we have all used Google Translate, autocorrect, or auto-complete at least once. Your email system’s spam filter uses NLP to determine which emails you should keep in your inbox and which ones should be deleted. These are only a few examples, and there are many more amazing applications for Natural Language Processing.
Natural Language Processing, or NLP, has grown in importance for business applications today. This is partially explained by the rise of Big Data, which primarily consists of unstructured text data. NLP has gained popularity thanks to the demand for clever methods to make sense of this text-heavy data.
What Exactly Is NLP?
Natural Language Processing (NLP) is a branch of AI technology that enables a machine to recognize and understand the subtleties of human language. Unstructured data is organized by examining it for relevance, spelling variations, correlation, and semantic meaning.
Like a human, it attempts to comprehend various lexicons, grammatical syntaxes, and the relationships between words and phrases.
Today, NLP is successfully used in speech pattern recognition, weather forecasting, healthcare, and document classification applications. We use so many NLP applications in business days that we don’t even realize how pervasive the technology is.
NLP Applications And Their Use Cases For Modern Enterprises
Thanks to significant advancements in data accessibility and a rise in computing power, AI and NLP are thriving today.
The following are a few of the actual uses of NLP in contemporary businesses:
- Text Classification
In business applications, categorizing content and documents helps with discovery, efficient document management, and insight extraction. The automatic labeling of documents and text units into recognized categories is known as text classification or document categorization. Text classification is used, for instance, when ten categories are automatically assigned to one or more of your company’s presentation documents.
For instance, using text classification techniques, LinkedIn flags user profiles that contain inappropriate content, such as profanity or advertisements for questionable services. On the other hand, Facebook employs text classification techniques to find hate speech on its platform.
One of the NLP applications in business that is most frequently used is text classification. However, to make text classification effective for your business, you must collect and store the correct data.
- Conversational Agents
Conversational agents use text, speech, or both to interact with users in natural language. Two types of conversational agents exist.
- Virtual Assistants
Virtual assistants, also known as digital assistants or AI assistants, are programmed to engage in brief conversations with users while carrying out specific tasks.
Examples of AI assistants include Siri, Alexa, and Google Assistant. These machines can call a friend, set an appointment in your calendar, look up restaurants, provide driving instructions, and turn on your TV because they can understand human commands. Such customer service representatives are also used by businesses to respond to simple customer inquiries or issues on their websites.
On the other hand, chatbots are made to have lengthy conversations with people. Instead of concentrating on a specific task, it mimics chats in human-to-human conversations.
- Sentiment Analysis
The automatic interpretation and summarization of emotions in text data are known as sentiment analysis. For instance, emotions can be “positive,” “negative,” or neutral when predicting them in Tweets. It can also be more specific, allowing you to pick up on things like anger, joy, sadness, and disgust.
Using online discussions or direct feedback, businesses can use sentiment analysis to understand how customers feel about particular brands, goods, and services. By doing this, companies can discover innovation opportunities and better understand their customer’s preferences.
- Information Retrieval (IR)
Finding documents from an extensive collection of documents that meet a user’s need is known as information retrieval or IR. The traditional illustration of an IR system is Google Search. It retrieves the data you require from the entire Web. Another illustration is the email search in Gmail.
While IR is regarded as a separate field of study in academia, it is viewed as a branch of NLP in the business world. This is so because IR deals with text data retrieval. NLP is also needed to comprehend a user’s keyword query.
- Extraction of Information
The process of extracting specific content from text is known as information extraction. Information extraction is potent when you need certain content hidden within vast blocks of text and images.
For instance, you can automate the process of adding appointments to your calendar by extracting appointment information from your emails. After reading the relevant emails, Google offers to add events like concert tickets or flight confirmations to your calendar.
Without a doubt, Natural Language Processing will shortly have a more significant impact on business. Companies are utilizing NLP to enhance performance by facilitating communication, gaining critical insight from unstructured data, and better understanding customer intent through sentiment analysis. Without tiring, NLP technology can process language-based data more quickly than humans.