NLP, which stands for “natural language processing,” is the study of making computers understand and analyse human language. Normal language processing (NLP) uses AI computations to understand and interact with text and dialogue.
NLP has recently been used in a number of real-world applications, such as sentiment analysis, chatbots, and speech recognition. Businesses in many different fields are using NLP to manage their customer service systems, expand their marketing efforts, and improve their products.
In particular, this piece looks at five ways that NLP is used in the real world: feeling analysis, chatbots, machine translation, text rundown, and speech recognition. These applications could change the way people use technology by making technology interactions more natural, easy to understand, and user-friendly.
Here are five practical ways that Natural Language Processing can be used
Analysis of Sentiment
NLP can be used to break down message data to figure out how the writer feels about a certain product, service, or brand. This is used for things like keeping an eye on social media, looking at customer reviews, and doing market research.
A common use of NLP is an opinion study of the stock market, where investors and traders look at how people in virtual reality feel about a certain stock or market. For example, buyers can use NLP to figure out how the market feels about a stock by looking at tweets or news stories about that stock. By looking at the language used in these sources, investors can figure out if they have good or negative things to say about the stock.
Research on market sentiment can help investors make better financial choices by giving them information about how the market feels and letting them change their strategies as needed. For example, if a stock is getting a lot of positive feedback, an investor might think about buying more shares. On the other hand, if the feedback is negative, they might go to the auction or stop buying.
Chatbots that can understand and respond to inquiries posed in natural language can be created using NLP. This is implemented in programmes like virtual assistants and customer support systems to help them appear more human to the user.
Using natural language processing (NLP), a bank or other financial institution may create a chatbot similar to ChatGPT to assist consumers with inquiries about their accounts, past transactions, and other money-related matters. Because the chatbot can understand and answer questions in everyday words,
With the help of natural language processing (NLP), text can be translated from one tongue to another. Other language-translating programs, such as Skype Translator and Google Translate, make use of this.
In a similar way, a multinational business might use natural language processing (NLP) to translate marketing materials and product descriptions from their native tongue into the languages of their target markets. This makes it easier for them to talk to people in different parts of the world.
Natural Language Processing (NLP) can be used to break up long articles and papers into shorter, clearer versions. Services like content curation, study paper summaries, and news aggregation make use of this.
Using NLP, a news aggregator can turn long news stories into shorter ones that are easier to read. Text summarization makes it possible for people to get a quick summary of the news without having to read the whole piece.
Natural Language Processing (NLP) makes it possible to turn spoken words into text. This lets you use voice-based interfaces and take notes. Virtual assistants, services that convert words into text, and other voice-based applications all use this.
A virtual assistant like Amazon’s Alexa or Google’s Assistant uses natural language processing (NLP) to understand spoken directions and answer questions in natural language. Users no longer have to type orders or questions for the assistant. Instead, they can just talk to it.
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